Internet DRAFT - draft-huang-alto-mowie-for-network-aware-app
draft-huang-alto-mowie-for-network-aware-app
ALTO Y. Jia
Internet-Draft Y. Zhang
Intended status: Standards Track Tencent
Expires: 26 April 2023 Y. R. Yang
Yale University
G. Li
CMRI
Y. Lei
Y. Han
Tencent
Sabine. Randriamasy
Nokia
23 October 2022
MoWIE for Network Aware Application
draft-huang-alto-mowie-for-network-aware-app-05
Abstract
With the deployment of 5G networks, cloud-based interactive services
such as cloud gaming have gained substantial attention. To ensure
users' quality of experience (QoE), a cloud interactive service may
require not only high bandwidth (e.g., high-resolution media
transmission) but also low delay (e.g., low latency and low lagging).
However, the quality perceived by a user with mobile and wireless
device may vary, as a function of many factors, and unhandled changes
can substantially compromise the user's QoE. In this document, we
investigate network-aware applications (NAA), which realize cloud-
based interactive services with improved QoE, by efficient
utilization of a solution named Mobile and Wireless Information
Exposure (MoWIE). In particular, this document demonstrates, through
realistic evaluations, that mobile network information such as MCS
(Modulation and Coding Scheme) can effectively expose the dynamicity
of the underlying network and can be made available to applications
through MoWIE; using such an information, the applications can then
adapt key control knobs such as media codec schemes, encapsulation,
and application layer processing to minimize QoE distortion. Based
on the evaluations, we discuss how the MoWIE features can define
extensions of the ALTO protocol, to expose more lower-layer and finer
grain network dynamics.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
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Table of Contents
1. Introduction of Network-aware Applications . . . . . . . . . 3
2. Use Cases of Network-Aware Application (NAA) . . . . . . . . 5
2.1. Cloud Gaming . . . . . . . . . . . . . . . . . . . . . . 5
2.2. Low Delay Live Show . . . . . . . . . . . . . . . . . . . 6
2.3. Cloud VR . . . . . . . . . . . . . . . . . . . . . . . . 6
2.4. Performance Requirements of these Use Cases . . . . . . . 6
3. Current (Indirect) Technologies on NAA . . . . . . . . . . . 7
3.1. Video Compression Based on ROI (Region of Interest) . . . 7
3.2. AI-based Adaptive Bitrate . . . . . . . . . . . . . . . . 8
4. Preliminary QoE Improvement Based on MoWIE . . . . . . . . . 9
4.1. MoWIE Architecture and Network Information Exposure . . . 9
4.2. RAN-assisted TCP optimization based on MoWIE . . . . . . 12
4.3. NAA QoE Test based on MoWIE . . . . . . . . . . . . . . . 12
4.4. ROI Detection with Network Information . . . . . . . . . 13
4.5. Adaptive Bitrate with Network Capability Exposure . . . . 15
4.6. Analysis of the Experiments . . . . . . . . . . . . . . . 17
5. Standardization Considerations of MoWIE as an Extension to
ALTO . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 24
7. Security Considerations . . . . . . . . . . . . . . . . . . . 24
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8. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 24
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 24
9.1. Normative References . . . . . . . . . . . . . . . . . . 24
9.2. Informative References . . . . . . . . . . . . . . . . . 24
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 27
1. Introduction of Network-aware Applications
With the deployment of 5G networks
[draft-ietf-dmm-5g-uplane-analysis], more applications are available
as remote cloud-based applications (e.g., cloud AR/VR/MR). Many
conventional interactive, daily business applications are also
becoming widely used as cloud applications, with the help of mobile
networks and cloud, e.g., cloud video conference. Especially, during
the coronavirus pandemic in 2020, many people had to stay at home and
work/study remotely, and the usage of cloud applications, including
cloud-based online courses, cloud-based conferencing, and cloud
gaming, have surged significantly.
To optimize QoE for end users using mobile networks (a.k.a., cellular
networks), many cloud applications utilize information about the
mobile network status, e.g., delay, bandwidth, and jitter, to
dynamically balance the generated media traffic and the rendering/
mixing in the cloud. Currently, such an application assumes the
network as a link and continuously uses client or server measurements
to detect network characteristics, and then adaptively changes its
network-related configuration parameters which may impact QoS
handling as well as logical functions to support the upper layer
application. However, when only application information is utilized,
the QoE that an application can achive can be limited in some cases.
First, information from the application side (i.e., the 3rd party
application server) may have relatively long delay. When a user
enters a location with a bad network connectivity, such as an
elevator or an underground garage, the application will not receive
such an information immediately. As a result, the buffer of video
application may have a high chance to run out. Then the screen will
freeze and users' QoE will be harmed. Besides, the application does
not have information about other users in the cell. Thus, it cannot
know how many resources it can get and when it will change. If other
users enter the cell and compete on the usage of the resource, the
application layer may misjudge the resource and request a high
bitrate. Then the delay will increase and QoE will drop. Some
information from the network layer like physical resource block (PRB)
information and utilization rate can help applications to describe
how many resources the user will get and how many users are competing
with it. Such an information is helpful to predict the network and
streaming videos. However, the application cannot get those kinds of
information yet. Because 5G network deployed by the mobile network
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operation which is normally in a different domain compared with
internet-based applications with 3rd party, the application server
may not be able to directly get the network internal status without
network exposure. Therefore, there have been continuous efforts in
3GPP enable network exposure to serve the 3rd party applications in a
better way in terms of network resource utilization and user's
experiences.
The 3GPP mobile networks are always adhering to standard solutions to
get network dynamic indicators that can be used by applications. In
3GPP, many IP-based QoS mechanisms are used. For example, ECN
[RFC3168] has been supported by the 4G radio station (eNB) to provide
CE(Congestion Encountered) information to the IMS application to
perform Adaptive Bitrate (ABR) [TS26.114]. The application can
downgrade the bit rate after receiving the CE indication, but does
not know the exact bit rate to be selected. DSCP [RFC2474] is used
to identify the QoS class and paging strategy [TS23.501], but
typically the application cannot dynamically change the DSCP to
improve the bit rate based on the network status by mapping DSCP with
4G QCI or 5G 5QI can be a good approach to realize end-to-end QoS.
DASH [MPEGDASH] is an MPEG standard widely used for the applications
to detect the throughput of the network based on the current
throughput and buffering states and adaptively select the next
segment of video streaming with a suitable bitrate in order to avoid
re-buffering. SAND-DASH [TS26.247] defines the mechanism that the
network/server can provide available throughput to the applications;
in such case, the better bitrate can be selected by DASH application.
There are some early works on network status exposure to TCP server,
which may also indirectly impact application layer optimization, but
did not consider 5G networks [Sprecher_mobile], [flinck_mobile] In 5G
cellular networks, network capability exposure has been specified to
allow the 5G system (5GS) to expose user device location and network
status towards the 3rd party application servers modeled as AF
(Application Function) [TS23.501]. In such a case, the AF can
request the 5GS to establish a dedicated QoS Flow to transport an IP
flow with the AF-provided QoS requirements. Via certain
measurements, network internal status including congestion can be
exposed and optimization can be carried out for the cellular network
[PBECC].
5G also can provide QNC (QoS Notification Control) to an AF if the
GBR(Guaranteed Bitrate) of the established GBR QoS Flow cannot be
fulfilled, and the AF can change the bitrate after receiving the QNC
notification. But the AF still does not know which bitrate to be
selected. So 5G enhances the QNC by providing a list of AQPs
(alternative QoS profiles). With AQP, a 5G network provides a subset
of supported AQPs with the QNC, and then the AF selects a bit rate
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from 5G network supported AQPs. In such a case, the GBR can be
fulfilled again if the radio state of the UE is changed. QoS
prediction is realized by network function inside 5GC to collect and
analyze the status and parameters from the 5G network entities, and
deliver the analytics results to an entity such as application
server.
However, both network capability exposure and QoS prediction
solutions are designed for 5G access and core networks, which cannot
cover the whole end-to-end network. How to enable the application
which locates the internal DN (data network) to be aware of the lower
layer networks within 5G network are an important area for both
industrial and academic researchers, that is addressed by MoWIE.
MoWIE is a solution that aims to realize real-time provisioning of
cellular radio network information by networks to applications, thus
helping service providers to achieve a better policy control and to
improve user experience. The benefits of the MoWIE concept/solution
have been experimented on several use cases, detailed in Section 4.1.
2. Use Cases of Network-Aware Application (NAA)
There are three typical NAAs, cloud gaming, low-delay live shows, and
cloud VR, whose QoE can be largely enhanced with the help of MoWIE.
2.1. Cloud Gaming
As mentioned above, cloud gaming is widely used, and this kind of
games requires low latency and highly reliable transmission of motion
tracking data from the user to the gaming server in the cloud, as
well as low latency and high data rate transmission of processed
visual content from gaming server cloud to the user devices. Cloud
gaming is regarded as one major killer application as well as traffic
contributor to wireless and cellular networks including 5G. The
major advantages of cloud gaming are easy & quick starting (no/less
need to download and install a high-volume software in the user
device), less cost and process load in the user device and it is also
regarded as an anti-cheating measure. Thus, cloud gaming has become
a competitive replacement for console gaming using cheaper PC or
laptop. To support high quality cloud gaming services, the
application needs to get the information from the network layer,
e.g., the data rate value or range which lower layer can provide in
order to perform rendering and encoding, during which the application
in the cloud can adopt different parameters to adjust the size of
produced visual content within a time period.
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2.2. Low Delay Live Show
In 2019, over 500 million active users were using online personal
live show services in China and there are 4 million simultaneous
online audience watching a celebrity's show. Low delay live show
requires close interaction between the application and the network.
Compared with conventional broadcast services, this service is
interactive, which means that audience can be involved and they are
able to provide feedback to the anchor. For example, a gaming show
broadcasts the gaming playing to all audiences, and it also requires
playing game interaction between the anchor and audiences. A delay
lower than 100 ms is desired. If the delay is too large, there will
be undesirable degradation on user experiences especially in a large-
scale show. To lower the latency and provide size-adjustable show
content, the application also requires real-time lower layer
information.
2.3. Cloud VR
Cloud VR data volume is large which is related to different parameter
settings like DoF (Degree of Freedom), resolution and adopted
rendering and compression algorithm. The rendering can be performed
at the cloud/network side or a mix of the cloud and the user device
side. Because the latency in cloud VR is even as low as 20 ms, the
application may need to interact with network to get the information
about the segmentation or transport block information, and these
lower layers information may be dependent on different layer 2 and
layer 3 wireless protocol designs.
2.4. Performance Requirements of these Use Cases
There are different bandwidth, latency and lagging requirements for
the above services which are characterized as parameter range. The
reason of using a range is because such requirements are related to a
group of parameter settings including resolution, frame rate (FPS,
frame per second) and the compression mechanism. We consider
1080p~4K as the resolution range, 60-120 FPS (Frames per second) as
the frame rate and H.265 as an example compression algorithm. The
end-to-end latency requirement is not only related to FPS but also
the property of the service, i.e., for weak interactive and strong
interactive services.
With the typical parameters setting, cloud gaming generally needs a
bandwidth of 20~60 Mbps; we also consider that significant lagging
happens when the latency is larger than 200 ms, depending on the
types of games (e.g. 40 ms for First Person Shoot games, 80 ms for
Action games, and 200 ms for Puzzle games). In order to avoid bad
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user experiences, lagging is better when it is lower, and can be as
low as zero (in an optimal QoE). For low latency live shows, 20~50
Mbps bandwidth may be needed and the end-to-end latency requirement
is less than 100 ms. Cloud VR service generally requires 100~500
Mbps bandwidth and 20~50 ms end-to-end latency. It is noted that
these values are dependent on the parameter settings and they are
provided to illustrate the order of magnitude of these parameters for
the aforementioned use cases. These value range may be updated
according to specific scenarios and requirements.
3. Current (Indirect) Technologies on NAA
The applications have tried to increase QoE with the help of network
information captured from the application layer to guess the network
dynamics, such as bitrate, buffer status, and packet loss rate.
For example, adaptive bitrate (ABR) and buffer control methods to
reduce delay, and application layer forward error scheme (AL-FEC) to
avoid packet losing are proposed. This document focuses on two novel
approaches, which have achieved good performance in practice. One is
video encoding based on ROI, the other is reinforcement learning
based adaptive bitrate.
3.1. Video Compression Based on ROI (Region of Interest)
A foveated mechanism [Saccadic] in the Human Visual System indicates
that only small fovea region captures most visual attention at high
resolution, while other peripheral regions receive little attention
at low resolution. And we call those regions which attract users
most, the regions of interest (ROI)[Fahad].
To predict human attention or ROI, saliency detection has been widely
studied in recent years, with applications in object recognition,
object segmentation, action recognition, image caption, image/video
compression, etc.
Since there exists the region of interest in a video, the cloud
server can give the ROI region higher rate while making other regions
a lower rate. As a result, the whole rate of the video is reduced
while the watching experience will not be harmed.
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This method means to detect the ROI and re-allocate the coding scheme
for interested and non-interested regions in order to save the
bandwidth without sacrificing user's QoE. In recent years, the ever-
increasing video size has become a big problem to applications. The
data rate of a cloud gaming video in 1080P can reach 25 Mbps, which
brings huge burden to the network, even for 5G network. Those ROI-
based video compression methods are mainly applied to high
concurrency networks to relive the burden of networks and then keep
QoE in an acceptable range.
However, current methods utilize application information like
application rate and application buffer size as the indicators to
roughly adjust the algorithm in interactive video services. That
information is hard to reflect the real-time network status
precisely. Therefore, it is hard to balance the QoE and bandwidth
saving in real-time scenario. More direct information is helpful for
those ROI methods to improve the performance.
3.2. AI-based Adaptive Bitrate
This method intends to reduce lagging and ensure acceptable picture
quality.
Applications such as video live streaming and cloud gaming employ
adaptive bitrate (ABR) algorithms to optimize user QoE [MPC][CS2P].
Despite the abundance of recently proposed schemes, state-of-the-art
AI based ABR algorithms suffer from a key limitation. They use fixed
control rules based on simplified or inaccurate models of the
deployment environment. As a result, existing schemes inevitably
fail to achieve optimal performance across a broad set of network
conditions and QoE objectives.
A reinforcement learning based ABR algorithm named Pensieve was
proposed [Hongzi] recently. Unlike traditional ABR algorithms that
use fixed heuristics or inaccurate system models, Pensieve's ABR
algorithms are generated using observations of the resulting
performance of past decisions across a large number of video
streaming experiments. This allows Pensieve to optimize its policy
for different network characteristics and QoE metrics directly from
experience. Over a broad set of network conditions and QoE metrics,
it has been proven that Pensieve outperformed existing ABR algorithms
by 12%~25%.
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For this method and those methods built upon this, it has been proven
that all information, including rate, download time, buffer size or
network level information which can reflect the performance, are
useful to reinforcement learning. Since those data can reflect the
network dynamics, they have been used to help the applications to
know how to change the rate and improve users' QoE.
However, all these data are obtained from the client side or the
server side. In reality, it is not easy to obtain such data in an
effective and efficient way. The lack of standardized approach to
acquire these data makes it difficult to make this usable for
different applications for large scale deployment. Meanwhile, since
these data reflect the real-time network status, they may change
rapidly and randomly, and hence can be hard to use a theoretical
model to characterize.
To summarize, current practices can make some improvements by
indirectly measuring network status and react in the application.
However, the network status data are not rich, direct, real-time;
they also lack predictability, especially when in the mobile and
wireless network scenarios, which results in long reaction delay or
high QoE fluctuations.
4. Preliminary QoE Improvement Based on MoWIE
4.1. MoWIE Architecture and Network Information Exposure
The fundamental idea of MoWIE is to achieve on demand and periodic
network information from network to applications, helping network
service providers to realize a better policy control and to improve
users' experience.
A possible MoWIE architecture includes three core components: the
Client Application, the Mobile Network and the Application Server.
The raw data are collected firstly from the radio network and core
network; further processing on these collected data and the exposure
of Network information are provided to the application Server.
An application server can send network information request about UE/
Cell level information and obtain the NIS response on network
information from the mobile network. After user data pre-processing,
the application server will make best use of the network information
to perform analytics and directly enhance the application functions
e.g. bit rate, latency, and jitter.
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Typically, the network information provided by MoWIE includes two
types of information as below:
Cell level Information:
* The number of Downlink PRBs (Physical Resource Blocks) occupied
during sampling period;
* the cell load;
* the downklink (DL) MAC data rate per cell;
* the channel status (e.g. RSRP (Reference Signal Received Power)
and CQI (Channel Quality Indicator));
* the DL data rate;
* the PDCP (Packet Data Convergence Protocol) buffer status;
UE level information (without privacy information):
* The Downklink Signal to Inference plus Noise Ratio (SINR);
* MCS: The index of Modulation and Coding Scheme (MCS);
* The number of packets occupied in PDCP buffer;
* The number of downlink PDCP Service Data Unit (SDU) packets;
* The number of lost PDCP SDU packets;
* The per UE downlink MAC data rate;
* the per-UE channel status (e.g. RSRP (Reference Signal Received
Power) and CQI (Channel Quality Indicator));
* the per-UE DL data rate;
* the per-UE PDCP (Packet Data Convergence Protocol) buffer status;
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The network information listed here can also be found in 3GPP (PRB
[TS38.211], cell load [TS38.300] PDCP for 5G [TS38.323] RSRP, RSRQ,
RSSI [TS38.331], MCS, CQI [TS38.214], The number of packets occupied
in PDCP buffer, the number of Downlink PDCP SDU packets, the number
of PDCP SDU packets lost, the per-UE PDCP buffer status [TS38.323]),
to demonstrate the potential benefits of MoWIE for network-
application integration over cellular network. Figure 3-1 and
Figure 3-2 list the data types correponding to the cell-level
information and UE-level information, respectively.
+----------------------+--------------------------+
|Cell-level Information| Data type/Range |
+----------------------+--------------------------+
| PRB | Uint16 |
+----------------------+--------------------------+
| CQI | Uint8 |
+----------------------+--------------------------+
| RSRP | Uint8 |
+----------------------+--------------------------+
| RSRQ | Uint8 |
+----------------------+--------------------------+
| Cell load | [0,1] |
+----------------------+--------------------------+
Figure 4-1: Cell level data type
+------------------------------------+---------------+
| UE-level Information |Data type/Range|
+------------------------------------+---------------+
| Downlink SINR | Uint16 |
+------------------------------------+---------------+
| MCS | Uint8 |
+------------------------------------+---------------+
| Downlink PDCP SDU packets | Uint8 |
+------------------------------------+---------------+
| PDCP SDU packets lost | Uint8 |
+------------------------------------+---------------+
| Packets occupied in PDCP buffer | [0,1] |
+------------------------------------+---------------+
| CQI | Uint8 |
+------------------------------------+---------------+
| RSRP | Uint8 |
+------------------------------------+---------------+
| RSRQ | Uint8 |
+------------------------------------+---------------+
Figure 4-2: UE level data type
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4.2. RAN-assisted TCP optimization based on MoWIE
The RAN information is used to assist TCP sending window adjustment
rather than traditional transport layer measurement and
acknowledgement. The RAN proactively predicts available radio
bandwidth and the buffer status per UE in a time granularity of RTT
level (e.g. 100 ms) and then piggybacks such information in TCP ACK.
We have conducted trial in real mobile networks. It is observed that
for the UE with good SINR, the throughput is significantly improved
by nearly 100%, and the UE with medium SINR can achieve approximately
50% gain.
4.3. NAA QoE Test based on MoWIE
Different from traditional video streaming, cloud gaming has no
buffer to accommodate and re-arrange the received data. It must
display the stream once the stream is received. Any late stream is
of no use for the player. Cloud gaming performs not well in the
existing public 4G network according to our actual measurements. The
end to end delay is often greater than 100 ms for a gaming client in
Shenzhen to a gaming server in Shanghai, coupled with the codec
delay. Here the delay is defined as the total delay from the user's
operation instruction to show the response picture on user's screen.
Once the network fluctuates, users will experience a longer delay.
The poor user experience is not only because of the relative low
network throughput, but also because that the server cannot adapt the
application logical policies (e.g., codec scheme and data bitrate).
The popularity of 4K and even higher resolution and increasing FPS
for cloud gaming and AR/VR services require both high bandwidth and
low latency in wireless and cellular networks. The increasing
resolution would incur a higher encoding and decoding delay.
However, users' tolerance to delay will not increase with the
resolution, which means the application needs to adapt to the network
dynamics in a more efficient way. The higher resolution, the larger
range of the rate adaptation can be used.
In this section, we make experiments based on the methods described
in section 3 to improve the QoE of cloud gaming. The performance
between network-aware and native non-network-aware mechanisms are
compared.
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4.4. ROI Detection with Network Information
The first experiment is based on the ROI detection. We will
investigate the impact of network perception.
Saliency detection method has successfully reduced the size of videos
and improve the QoE of users in video downloading [Saliency].
However, it is not effective when applied to real-time interactive
streaming such as cloud gaming.
As we know, more accurate saliency region detection algorithm needs
more time to obtain the result. However, when the users are
suffering a bad performance network in cloud gaming, this precise
detection may incur more delay to the system. As a result, it will
harm the final QoE.
If the application can learn the network well in a real-time manner,
it can choose the algorithm based on how much delay the system can
tolerate. If the network condition is good enough, it can adopt an
algorithm which has deeper learning network and the added delay will
not be perceived by the end users. Thus, it can save huge bandwidth
without harming the QoE. On the other side, in a network with bad
condition, the server can use the fastest method to avoid extra
delay.
We make the experiments to show how the network information will
influence the total QoE and bandwidth saving in ROI detection.
The following 4 methods are compared:
1) The original video, without using ROI method. This acts as a
baseline.
2) Quick saliency detection and encoding method, which is not
accuracy in some cases. It only brings 10ms delay.
3) A relative accuracy saliency detection method. In general, if an
algorithm is more precise, it will take more time to get the results.
And the complexity of the picture will also influence the detection
time and accuracy. Based on our test video, we adopt the method
which brings delay about 40~70ms.
4) The application server in the cloud has the current bandwidth
information which derived from the wireless LAN NIC. Here it is a
simulation that all the collected bandwidth traces are already known
by the server. Thus, it can use the bandwidth traces to compute
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transmission delay. Then the server can change the saliency
detection algorithm based on this information and then encode the
video.
Although the result of future bandwidth prediction is not always
accurate in real environment, the assumption here will not influence
the final results much. Since in cloud gaming the server encodes the
stream based on ROI information frame by frame instead of in a grain
of chunks, the future bandwidth prediction window size doesn't have
to be long. Therefore, even the server can only get the bandwidth or
delay prediction for a short time window, the server can still use
this method with network information.
Test environment:
A 720P game video segment with a rate of 6.8Mbps. This is not a very
high bandwidth requirement example in cloud gaming. We just show how
it will benefit from MoWIE. High bandwidth requirement case will
benefit more if the bandwidth fluctuates much.
The three different networks are all wireless networks and the
available bandwidth is varied frequently, where Network 1: The
overall network condition is not very good, the average network
bandwidth is 7.1Mbps, but it continues to fluctuate, and the minimum
is only 3.9Mbps.
Network 2: The overall network condition is good, with an average
network bandwidth of 12Mbps and a minimum of 6.4Mbps.
Network 3: The network fluctuates dramatically, with an average
network bandwidth of 8.4Mbps and a minimum network bandwidth of
3.7Mbps
Test content:
The four methods are conducted on the original video under each three
networks. After re-encoding based on the saliency detection, we
calculate the new QoE and the saved bandwidth. The results are shown
in the Figure 4-1 (The QoE value is the MOS as standardized in the
ITU):
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+---+-----------------+-----------------+-----------------+
| | Network 1 | Network 2 | Network 3 |
+---+---+-------------+---+-------------+---+-------------+
| |QoE| BW Saving |QoE| BW Saving |QoE| BW Saving |
+---+---+-------------+---+-------------+---+-------------+
| 1 |3.8| 0 |4.8| 0 |4.3| 0 |
+---+---+-------------+---+-------------+---+-------------+
| 2 |3.8| 5% |4.8| 9% |4.3| 7% |
+---+---+-------------+---+-------------+---+-------------+
| 3 |2.2| 2.1% |4.6| 38% |3.1| 34% |
+---+---+-------------+---+-------------+---+-------------+
| 4 |3.6| 9% |4.7| 33% |4.3| 25% |
+---+---+-------------+---+-------------+---+-------------+
Figure 4-3: QoE and Bandwidth Saving
Conclusion:
It can be seen that the methods such as method 2 and method 3 that do
not rely on the network information directly, have certain
limitations.
Though the method 2 is simple and time-consuming, it can only detect
a small part of region of interest accurately. Thus, even if the
network condition is very good, it can only save a small amount of
bandwidth, and sometimes there are some incorrect ROI detection. The
QoE will be reduced without hitting the ROI region.
For Method 3, the algorithm is complicated, and it can correctly
detect the user's area of interest, so that it can re-allocate
encoding scheme and save a lot of bandwidth. However, its algorithm
will introduce higher delay. When the user network condition is
poor, the extra delay will cause even worst user's QoE. Although the
bandwidth is saved, it affects the user experience seriously.
Method 4 is based on the application's awareness of the network. If
the application can know certain network information, it can balance
the complexity of the algorithm (introducing delay) and the accuracy
of the algorithm (saving bandwidth) according to the actual network
conditions. As can be seen from the experiment, method 4 can ensure
the user's QoE and save the bandwidth greatly at the same time.
4.5. Adaptive Bitrate with Network Capability Exposure
This experiment is AI-based rate adaption by utilizing the network
information provided by the cellular base station (gNB) in cellular
network.
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Tencent has launched real network testing of NAA-enabled cloud gaming
in China Mobile LTE network, with the enhancement in gNB supporting
base station information exposure.
To enable the NAA mechanism, some cellular network information from
gNBs are collected in an adaptive interval based on the change rate
of network status. There information is categorized in two levels,
i.e., cell level and UE level. Cell level information are common for
all the UEs under a serving LTE cell and UE level information is
specific for different UEs. 3GPP LTE specifications have specified
how the PDCP, RLC (Radio Link Control), MAC (Medium Access Control)
and PHY (Physical) protocols operate and this information are very
essential statistics from these protocol layers.
It is noted that in NAA mechanism, as the network information is from
gNB, and the gNB has the real-time information of radio link quality
statistics and layer 1 and layer 2 operation information, NAA
mechanism can expose rich information to upper layer, e.g., it is
capable to differentiate packet loss and congestion [MengZ], which is
very helpful to the applications in practice.
In order to compare the cases with and without NAA, the cloud gaming
test environment is setup with 1080p resolution and around 20Mbps
bitrate.
Test scenarios 1~5 are as follows.
Test scenarios 1: Weak network. This scenario is the case where
radio link quality is low, e.g., in cell edge area and the bandwidth
is not able to serve cloud gaming.
Test scenario 2: User competition scenario. This scenario is defined
as the case when user amount is large thus the cellular network
bandwidth cannot serve all the cloud gaming users.
Test scenario 3-5: Other scenarios with random user movement trace
and user distribution.
Test method: To simplify to comparison, we just use the MCS (MCS
index) information derived from the gNB [TS38.214]. The information
is provided directly to the application, and the application then
adjusts the bit rate according to this information. Here, MCS index
shows the modulation (e.g. QPSK, 16QAM,...) and the coding rate used
during physical layer transmission, which is relevant to the real
data rate per UE. The benchmark method is adopting a constant bit
rate without any information to help it predicting the network
condition. We compare these scenarios and observe the reduction of
delay when those gNB data are utilized.
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For different scenarios, the lagging rate is defined as the
performance indicator. In our experiments, we assume lagging happens
when transmission delay is greater than 200ms and lagging rate is
defined as the ratio between the number of frames greater than 200ms
and the total number of frames.
+-------------+--------------------------+
|Test Scenario| Reduction of Lagging Rate|
+-------------+--------------------------+
| 1 | 46% |
+-------------+--------------------------+
| 2 | 21% |
+-------------+--------------------------+
| 3 | 37% |
+-------------+--------------------------+
| 4 | 56% |
+-------------+--------------------------+
| 5 | 32% |
+-------------+--------------------------+
Figure 4-4: Reduction of Lagging Rate
It can be clearly seen that with the MCS information, the application
can adjust the bit rate to decrease the lagging rate and then
significantly improve the user QoE. In weak network scenario, 46%
lagging can be avoided by NAA.
4.6. Analysis of the Experiments
The above-mentioned technologies demonstrate the performance gain of
NAA with MoWIE.
Although application information can also help to predict the network
and have already been used in adaptive bit rate methods, the
application information is not as sensitive as gNB information at the
very beginning in a lot of cases. For example, when more users enter
the cell, the PRB information will first reflect that each user may
get less bandwidth. However, the application information needs to
react after there is a trend that the bitrate is decreasing. That is
to say, the lower layer network information is more directly.
Without MoWIE, the application cannot get the lower layer network
information directly and then try to detect "blindly" to adapt to the
dynamics of the lower layer network, which cannot meet the
requirements of cloud interactive applications like cloud gaming, low
delay live show and Cloud VR.
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It is noted that the more real-time network resource status the
application can learn, the better it can predict how much network
resource it can use within a prediction time window. However, there
is tradeoff between network information collection frequency and its
load and feasibility to the network devices. In principle, the total
network resource consumed for such network status reporting is also
designed in light-weight manner, e.g., by properly controlling the
interval of report and also the number of bits needed to convey the
reported information elements. In our experiments, the network
status information can be obtained in an adaptive interval based on
the change rate of network status, in order to provide good
prediction with less load introduced in the network. In fact, not
all scenarios need a very frequent information collection. If some
information only changes in a very small range and won't influence
the final decision, it is unnecessary to report such information all
the time. However, if its value varies over the preset threshold, it
will inform the application immediately.
The distribution and impact of the exposed data to the performance
gain for different algorithm needs to be further studied. This draft
is to give a guidance to figure out what kind of data needs to be
exposed during initial deployment of these mechanisms.
In our current cloud gaming, the application information can help to
reduce about 50% the lagging rate. The left 50% improvement room can
be achieved by network information exposure with MoWIE. Actually,
the effect of the two-layer information can be accumulated. However,
due to current deployment limitation, we cannot collect the
application information with the gNB information at the same time.
Thus, in this version of the draft we compare the performance with
and without MoWIE. We don't compare between application information
assisted mode and network information assisted mode in this draft.
This is our on- going work. Since both application and gNB
information can reflect the network variation, we will compare the
performance among application information assisted mode, network
information assisted mode and the mode of utilizing both layer
information.
5. Standardization Considerations of MoWIE as an Extension to ALTO
In 3GPP, network information exposure based on control plane
mechanism is introduced in 4G and 5G systems. In 3GPP Release 17,
there is a work item named 5G_AIS (Advanced Interactive Services)
which focuses on QoS enhancements for interactive services including
cloud gaming, XR, remote driving and real-time digital twin. There
is also continuous work in Release 18 XRM (XR and media services),
which focuses on strengthening the interaction and collaboration
between applications and networks by improving the ability of the
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network information exposure especially for XR and multimedia
services. MoWIE is closedly related to this study as it can be a
tool to support exposure of 5G network status information. MoWIE can
also support QoS prediction technologies which is being applied to
5G-enabled automated driving which is being developed in 5GAA
[PQoS_white_paper], this solution can use network information
exposure to predict the future network changes to the application
layer in order to be prepared for such changes and make adaptations
in application layer to improve the QoE of users. There have been
close collaboration between 5GAA and 3GPP to provide E2E solutions on
this area.
Among these above mentioned work items, one important way to support
QoS (MoWIE and PQoS) enhancements is to expose the network status
information to the application layer and the application layer can
take measures to adapt according to the network status information.
The network information can include the radio network statistics as
has been elaborated in Section 4.1 and also the parameters specified
in [ALTO_METRICS]. In Section 4.1, the parameters which MoWIE
proposes to expose can provide real-time status and rich information
about the wireless link which can be utilized by AI-ML (Machine
Learning) algorithms to predict the available network resources in
the subsequent transmission opportunities, which can help the
application layer to adjust its traffic pattern or codec profile to
optimize the user's experience. By mapping these parameters with the
ALTO metrics which has been proposed or defining potential new ALTO
metrics, it is possible to extent current ALTO protocols to provide
better support for real-time immersive services. In ETSI MEC, RNIS
[ETSI_MEC] has proposed to expose physical layer, Layer 2 and higher
layer parameters including 4G and 5G. There are some common
parameters like RSRP, RSRQ and RSSI which MoWIE also proposes;
however, RNIS is based on the MEC architecture, and MoWIE is not
restricted to MEC case.
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It should be noticed that the previous mechanisms may also work on
IEEE 802.11 standards (e.g., EHT), helping SP to have a better
understanding for the network environment between AP and STAs. Based
on the fact that 802.11 devices are working on unlicensed spectrums,
and easily influenced by adjacent unlicensed devices, duty cycles and
related CQI information (e.g., MCS, and bandwidth) are considered
very important network information here. Standardization
Considerations of MoWIE as an Extension to ALTO MoWIE can be a
realistic, important extension to ALTO to serve the aforementioned
use cases, in the setting of the newer generation (5G) of cellular
network, which is a completely open IP based network where routers/
UPF with IP connectivity will be deployed much closer to the users.
One may consider not only the aforementioned cloud-based multimedia
applications, but also other latency sensitive applications such as
connected vehicles and automotive driving.
Extending ALTO with MoWIE, therefore, may allow ALTO to expose lower
layer network information to ensure higher application QoE for a wide
spectrum of applications.
One possible approach to standardizing the distribution of the
network information used in the evaluations is to send such
information as piggyback information in the data path. One issue
with data path method is that MoWIE intends to convey more complex
and rich information than current methods. To piggyback such complex
and rich information in the data path will consume significant data
path resource. But the data path-based method can provide frequently
changed network information and it is technically simpler to
synchronize the network information and user data at the same time
scale. Normally, there is less user data in the uplink direction and
the free "space" within the MTU can be used to piggyback the network
information to the application, without the additional overhead of
creating a second communication channel between the application and
network. However, the data path design may bring out more limited
privacy management, which is very important in MoWIE. The
application cannot trust the network information if there is no
message authentication mechanism for the piggyback network
information. How the network inserts the network information in the
data packet is also challengeable since a lot of transport layer
protocol are encrypted and integration protected. Another method is
to create an associated path aligned with data path. Like the ICMP
for IP and RTCP for RTP, this second path can be used to provide
additional information associated with the data path. But creating
such second path is a big change to current widely used transport
protocols and a lot of applications also need to change, this second
path is also challengeable.
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In this draft, we mainly discuss ALTO extension-based design in
tackling with this problem. Specifically, the MoWIE extension will
reuse existing ALTO mechanisms including information resource
directory, extensible performance metrics and calendaring, and
unified properties. Below is an overview of key considerations;
security considerations are in the following section.
* Network information selection and binding consideration: Instead
of hardcoding only specific network information, a modular design
of MoWIE is an ability for an ALTO client to select only the
relevant information (e.g., cell DLOccupyPRBNum metric and UE MCS)
and then request correspondingly. Existing ALTO information
resource directory is a starting point, but the design needs to be
generic," to provide abstraction for ease of use and
extensibility. The security mechanisms of the existing ALTO
protocol should also be extended to enforce proper authorization.
* Compact network information encoding considerations: One benefit
of ALTO is its high-level JSON based encoding. When the update
frequency increases, the existing base protocol and existing
extensions (in particular the SSE extension), however, may have
high bandwidth and processing overhead. Hence, encoding and
processing overhead of MoWIE should be considered.
* Stability and reliability considerations: A key benefit of the
MoWIE extension is the ability to allow more flexible, better
coordinated control. Any control mechanism, however, should
integrate fundamental overhead, stability, and reliability
mechanisms.
* Cost metrics considerations. In [ALTO_METRICS], some cost metrics
are being standardized including throughput/bit rate, latency,
priority, error rate, jitter. These parameters can be linked with
cost metrics in 5G network entities like network exposure function
(NEF) [TS23.501] or application function (AF) [TS23.501]. NEF or
AF, which act as ALTO Clients, utilizing the network information
exposure capability provided by 3GPP standards, can request to
expose some of the proposed parameters with consideration of the
ALTO performance metrics.
With the better utilization of the 5G network, IETF ALTO can well
benefit from MoWIE architecture and QoE for the use cases of NAA
applications can be achieved with the help of the convergence of 5G
network architecture and IETF ALTO. For example, it was shown in
Figure 5-1 that the ALTO client can act as AF or act as part of the
functional module of AF to converge with the 5G system through the
implementation in the AF. It can receive the cellular-based network
information through interaction with NEF, identify the ALTO server
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using ALTO service discovery, and request available ALTO information
prepared by the ALTO server using ALTO Protocol [RFC7285]. The ALTO
server can be implemented in other places through ALTO architecture
and can interact with the third parties (e.g. Content providers) via
external interfaces. Furthermore, the ALTO server can aggregate
information from multiple functional entities to provide an abstract
and unified view that can be more useful to applications. Examples
of other systems include (but are not limited to) static network
configuration databases, dynamic network information, routing
protocols, provisioning policies, and interfaces to outside parties
[RFC7285]. Please note that these components shown in Figure 5-1 are
out of IETF ALTO scope and are just for completeness to put here. As
a result, the ALTO Client can perform ALTO Service Discovery and
interact with the ALTO server via ALTO Protocol to get the ALTO
information and ALTO metrics regarding the proposed parameters with
the help of MoWIE that can be updated dynamically based on network
conditions from cellular aspects in Section 4.1. It is noted that in
addition to AF-based convergence architecture which relies on
control-plane interfaces e.g. N5/N33, MoWIE can also utilized user
plane exposure via N6 or UPF which makes network exposure mechanisms
more comprehensive and flexisble.
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+-----------------+ +------------+ +--------------+
| Dynamic Network | | Routing | | Provisioning |
| Information | | protocols | | policy |
+---------+-------+ +------+-----+ +------+-------+
| | |
+-----------------|---------------+
|
+-----------+ +---------+
| External | | ALTO |
| Interface |--------| Server |
+-----+-----+ +----+----+
| |
| | ALTO protocol
| |
+-------------+ +-------^--------+
| 3rd content | | ALTO Client/AF |
| provider | +----------------+
+-------------+ |---------------+
| | |
| |N33 |N5
| +----------+ +---------+
| | NEF | | PCF |
| +----------+ +---------+
| | |N7
| |---------------+
| |
| +----------+ N11 +-------+
| | SMF |-----| AMF |
| +----------+ +-------+
| |N4 |N2
| | |
+----------+ | |
| Data | N6 +-----------+ N3 +--------+ +------+
| Network |-------| UPF |-----| RAN |---| UE |
+----------+ +-----------+ +--------+ +------+
Figure 5-1: Convergence of 5G network architecture and IETF ALTO
By extending the exposure scope of network information beyond the
cellular access, ALTO can help improve the QoE of several
applications running on endpoints located in cellular networks.
[ALTO_USE_CASES] is work in progress that investigates use cases
where the performances of these applications can be further improved
with abstracted network information and suitable transportation means
provided by ALTO. Additionally, upon reviewing the existing ALTO
capabilities, it lists the ALTO features that need to be extended or
defined to support the presented use cases.
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6. IANA Considerations
This document has no actions for IANA.
7. Security Considerations
The collection, distribution of MoWIE information should consider the
security requirements on information privacy and information
integration protection and authentication in both sides. Since the
network status is not directly related to any special user, there is
currently no any privacy issue. But the information transmitted to
the application can pass through a lot of middle box and can be
changed by the man in the middle. To protect the network
information, an end to end encryption and integration is needed.
Also, the network needs to authenticate the information exposure
provided to right applications. These security requirements can be
implemented by the TLS and other security mechanisms.
8. Acknowledgments
The authors would like to thank Huang Wei and Mohamed Boucadair for
their contribution and technical review comments to the previous
versions of this draft.
9. References
9.1. Normative References
[RFC2474] Nichols, K., Blake, S., Baker, F., and D. Black,
"Definition of the Differentiated Services Field (DS
Field) in the IPv4 and IPv6 Headers", RFC 2474,
DOI 10.17487/RFC2474, December 1998,
<https://www.rfc-editor.org/info/rfc2474>.
[RFC3168] Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
of Explicit Congestion Notification (ECN) to IP",
RFC 3168, DOI 10.17487/RFC3168, September 2001,
<https://www.rfc-editor.org/info/rfc3168>.
[RFC7285] Alimi, R., Ed., Penno, R., Ed., Yang, Y., Ed., Kiesel, S.,
Previdi, S., Roome, W., Shalunov, S., and R. Woundy,
"Application-Layer Traffic Optimization (ALTO) Protocol",
RFC 7285, DOI 10.17487/RFC7285, September 2014,
<https://www.rfc-editor.org/info/rfc7285>.
9.2. Informative References
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[ALTO_METRICS]
"Internet-Draft, draft-ietf-alto-performance-metrics-09",
" ALTO Performance Cost Metrics", 2020,
<https://tools.ietf.org/html/draft-ietf-alto-performance-
metrics-09>.
[ALTO_USE_CASES]
"Internet-Draft, draft-li-alto-cellular-use-cases-00",
" Requirements and reference architecture for Mobile
Throughput Guidance Exposure", 2021,
<https://datatracker.ietf.org/doc/html/draft-li-alto-
cellular-use-cases>.
[CS2P] Sun, Yi., Yin, Xiaoqi., Jiang, Junchen., Sekar, Vyas.,
Lin, Fuyuan., Wang, Nanshu., Liu, Tao., and Bruno.
Sinopoli, "CS2P: Improving Video Bitrate Selection and
Adaptation with Data-Driven Throughput Prediction",
DOI 10.1145/2934872.2934898, 2016,
<https://doi.org/10.1145/2934872.2934898>.
[draft-ietf-dmm-5g-uplane-analysis]
"Internet-Draft, draft-ietf-dmm-5g-uplane-analysis-04",
" ALTO Uses Cases for Cellular Networks", 2020,
<https://datatracker.ietf.org/doc/html/draft-ietf-dmm-5g-
uplane-analysis-04#section-4.1>.
[ETSI_MEC] "ETSI GS MEC 012", "Multi-access Edge Computing
(MEC); Radio Network Information API", 2019,
<https://www.etsi.org/deliver/etsi_gs/MEC/>.
[Fahad] Fazal Elahi Guraya, Fahad., Alaya Cheikh, Faouzi., and
Victor. Medina, "A Novel Visual Saliency Model for
Surveillance Video Compression",
DOI 10.1109/SITIS.2011.84, 2011,
<https://doi.org/10.1109/SITIS.2011.84>.
[flinck_mobile]
"Internet-Draft, draft-flinck-mobile-throughput-guidance-
04", " User Plane Protocol and Architectural Analysis on
3GPP 5G System", 2017,
<https://datatracker.ietf.org/doc/html/draft-flinck-
mobile-throughput-guidance-04>.
[Hongzi] Mao, Hongzi., Netravali, Ravi., and Mohammad. Alizadeh,
"Neural Adaptive Video Streaming with Pensieve",
DOI 10.1145/3098822.3098843, 2017,
<https://doi.org/10.1145/3098822.3098843>.
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[MengZ] Meng, Z., Guo, Y., Sun, C., Wang, B., Sherry, J., Liu, H.
H., Xu, M. (2022), "Achieving Consistent Low Latency for
Wireless Real-Time Communications with the Shortest
Control Loop", DOI 10.1145/3544216.3544225, 2022,
<https://zilimeng.com/papers/zhuge-sigcomm22.pdf>.
[MPC] Yin, Xiaoqi., Jindal, Abhishek., Sekar, Vyas., and Bruno.
Sigopoli, "A Control-Theoretic Approach for Dynamic
Adaptive Video Streaming over HTTP",
DOI 10.1145/2785956.2787486, 2015,
<https://doi.org/10.1145/2785956.2787486>.
[MPEGDASH] ISO/IEC, "ISO/IEC 23009, Dynamic Adaptive Streaming over
HTTP", 2020,
<https://mpeg.chiariglione.org/standards/mpeg-dash>.
[PBECC] Xie, Yaxiong, Fan Yi, and Kyle Jamieson, "PBE-CC:
Congestion control via endpoint-centric, physical-layer
bandwidth measurements", DOI 10.1145/3387514.3405880,
2020,
<https://dl.acm.org/doi/pdf/10.1145/3387514.3405880>.
[PQoS_white_paper]
"Making 5G Proactive and Predictive for the Automotive
Industry", " 5GAA Automotive Association, Tech. Rep.",
2020, <https://5gaa.org/wp-content/
uploads/2020/01/5GAA_White-Paper_Proactive-and-
Predictive_v04_8-Jan.-2020-003.pdf>.
[Saccadic] Matin, E., "Saccadic suppression: A review and an
analysis", DOI 10.1037/h0037368, 1974,
<https://doi.org/10.1037/h0037368>.
[Saliency] Guo, C. and L. Zhang, "A Novel Multiresolution
Spatiotemporal Saliency Detection Model and Its
Applications in Image and Video Compression",
DOI 10.1109/TIP.2009.2030969", 2017,
<https://doi.org/10.1109/TIP.2009.2030969>.
[Sprecher_mobile]
"Internet-Draft, draft-sprecher-mobile-tg-exposure-req-
arch-03", " Mobile Throughput Guidance Inband Signaling
Protocol", 2017, <https://datatracker.ietf.org/doc/html/
draft-sprecher-mobile-tg-exposure-req-arch-03>.
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[TS23.501] "3rd Generation Partnership Project (3GPP)",
"System architecture for the 5G System (5GS)", 2021,
<https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3144>.
[TS26.114] "3rd Generation Partnership Project (3GPP)",
"IP Multimedia Subsystem (IMS); Multimedia telephony;
Media handling and interaction", 2021,
<https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=1404>.
[TS26.247] "3rd Generation Partnership Project (3GPP)",
"Progressive Download and Dynamic Adaptive Streaming over
HTTP(3GP-DASH)", 2020,
<https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=1444>.
[TS38.211] "3rd Generation Partnership Project (3GPP)", "NR; Physical
channels and modulation", 2017,
<https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3213>.
[TS38.214] "3rd Generation Partnership Project (3GPP)", "NR; Physical
layer procedures for data", 2021,
<https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3216>.
[TS38.300] "3rd Generation Partnership Project (3GPP)", "NR; NR and
NG-RAN Overall description; Stage-2", 2017,
<https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3191>.
[TS38.323] "3rd Generation Partnership Project (3GPP)", "NR; Packet
Data Convergence Protocol (PDCP) specification", 2017,
<https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3196>.
[TS38.331] "3rd Generation Partnership Project (3GPP)", "NR; Protocol
specification", 2017,
<https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3197>.
Authors' Addresses
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Yuhang Jia
Tencent
Flat 9, No. 10 West Building, Xi Bei Wang East Road
Beijing
100090
China
Email: tonyjia@tencent.com
Yunfei Zhang
Tencent
Flat 9, No. 10 West Building,Xi Bei Wang East Road
Beijing
100090
China
Email: yanniszhang@tencent.com
Y. Richard Yang
Yale University
Watson 208A, 51 Prospect Street
New Haven, CT 06511
United States of America
Email: yang.r.yang@yale.edu
Gang Li
China Mobile Research Institute
No.32, Xuanwumenxi Ave, Xicheng District
Beijing
100053
China
Email: ligangyf@chinamobile.com
Yixue Lei
Tencent
Flat 9, No. 10 West Building,Xi Bei Wang East Road
Beijing
100090
China
Email: yixuelei@tencent.com
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Internet-Draft MoWIE for Network Aware Application October 2022
Yunbo Han
Tencent
Tencent Building, No. 10000 Shennan Avenue, Nanshan District
Shenzhen
518000
China
Email: yunbohan@tencent.com
S. Randriamasy
Nokia
Nokia Bell Labs
Nozay
United States of America
Email: sabine.randriamasy@nokia-bell-labs.com
Jia, et al. Expires 26 April 2023 [Page 29]
