Internet DRAFT - draft-ietf-mops-ar-use-case
draft-ietf-mops-ar-use-case
MOPS R. Krishna
Internet-Draft InterDigital Europe Limited
Intended status: Informational A. Rahman
Expires: 5 September 2024 Ericsson
4 March 2024
Media Operations Use Case for an Extended Reality Application on Edge
Computing Infrastructure
draft-ietf-mops-ar-use-case-15
Abstract
This document explores the issues involved in the use of Edge
Computing resources to operationalize media use cases that involve
Extended Reality (XR) applications. In particular, this document
discusses those applications that run on devices having different
form factors (such as different physical sizes and shapes) and need
Edge computing resources to mitigate the effect of problems such as a
need to support interactive communication requiring low latency,
limited battery power, and heat dissipation from those devices. The
intended audience for this document are network operators who are
interested in providing edge computing resources to operationalize
the requirements of such applications. This document discusses the
expected behavior of XR applications which can be used to manage the
traffic. In addition, the document discusses the service
requirements of XR applications to be able to run on the network.
Status of This Memo
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provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
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This Internet-Draft will expire on 5 September 2024.
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Copyright Notice
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document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Use Case . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1. Processing of Scenes . . . . . . . . . . . . . . . . . . 4
2.2. Generation of Images . . . . . . . . . . . . . . . . . . 5
3. Technical Challenges and Solutions . . . . . . . . . . . . . 5
4. XR Network Traffic . . . . . . . . . . . . . . . . . . . . . 7
4.1. Traffic Workload . . . . . . . . . . . . . . . . . . . . 7
4.2. Traffic Performance Metrics . . . . . . . . . . . . . . . 8
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 10
6. Security Considerations . . . . . . . . . . . . . . . . . . . 10
7. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 10
8. Informative References . . . . . . . . . . . . . . . . . . . 10
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15
1. Introduction
Extended Reality (XR) is a term that includes Augmented Reality (AR),
Virtual Reality (VR) and Mixed Reality (MR) [XR]. AR combines the
real and virtual, is interactive and is aligned to the physical world
of the user [AUGMENTED_2]. On the other hand, VR places the user
inside a virtual environment generated by a computer [AUGMENTED].MR
merges the real and virtual world along a continuum that connects
completely real environment at one end to a completely virtual
environment at the other end. In this continuum, all combinations of
the real and virtual are captured [AUGMENTED].
XR applications will bring several requirements for the network and
the mobile devices running these applications. Some XR applications
such as AR require a real-time processing of video streams to
recognize specific objects. This is then used to overlay information
on the video being displayed to the user. In addition, XR
applications such as AR and VR will also require generation of new
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video frames to be played to the user. Both the real-time processing
of video streams and the generation of overlay information are
computationally intensive tasks that generate heat [DEV_HEAT_1],
[DEV_HEAT_2] and drain battery power [BATT_DRAIN] on the mobile
device running the XR application. Consequently, in order to run
applications with XR characteristics on mobile devices,
computationally intensive tasks need to be offloaded to resources
provided by Edge Computing.
Edge Computing is an emerging paradigm where computing resources and
storage are made available in close network proximity at the edge of
the Internet to mobile devices and sensors [EDGE_1], [EDGE_2]. These
edge computing devices use cloud technologies that enable them to
support offloaded XR applications. In particular, the edge devices
deploy cloud computing implementation techniques such as
disaggregation (using SDN to break vertically integrated systems into
independent components- these components can have open interfaces
which are standard, well documented and not proprietary),
virtualization (being able to run multiple independent copies of
those components such as SDN Controller apps, Virtual Network
Functions on a common hardware platform) and commoditization ( being
able to elastically scale those virtual components across commodity
hardware as the workload dictates) [EDGE_3]. Such techniques enable
XR applications requiring low-latency and high bandwidth to be
delivered by mini-clouds running on proximate edge devices. This is
because the disaggregated components can run on proximate edge
devices rather than on remote cloud several hops away and deliver low
latency, high bandwidth service to offloaded applications [EDGE_2].
This document discusses the issues involved when edge computing
resources are offered by network operators to operationalize the
requirements of XR applications running on devices with various form
factors. Examples of such form factors include Head Mounted Displays
(HMD) such as Optical-see through HMDs and video-see-through HMDs and
Hand-held displays. Smart phones with video cameras and location
sensing capabilities using systems such as a global navigation
satellite system (GNSS) are another example of such devices. These
devices have limited battery capacity and dissipate heat when
running. Besides as the user of these devices moves around as they
run the XR application, the wireless latency and bandwidth available
to the devices fluctuates and the communication link itself might
fail. As a result algorithms such as those based on adaptive-bit-
rate techniques that base their policy on heuristics or models of
deployment perform sub-optimally in such dynamic environments
[ABR_1]. In addition, network operators can expect that the
parameters that characterize the expected behavior of XR applications
are heavy tailed. Heaviness of tails is defined as the difference
from the normal distribution in the proportion of the values that
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fall a long way from the mean [HEAVY_TAIL_3]. Such workloads require
appropriate resource management policies to be used on the Edge. The
service requirements of XR applications are also challenging when
compared to the current video applications. In particular several
Quality of Experience (QoE) factors such as motion sickness are
unique to XR applications and must be considered when
operationalizing a network. This document motivates these issues
with a use-case that is presented in the following sections.
2. Use Case
A use case is now described that involves an application with XR
systems' characteristics. Consider a group of tourists who are being
conducted in a tour around the historical site of the Tower of
London. As they move around the site and within the historical
buildings, they can watch and listen to historical scenes in 3D that
are generated by the XR application and then overlaid by their XR
headsets onto their real-world view. The headset then continuously
updates their view as they move around.
The XR application first processes the scene that the walking tourist
is watching in real-time and identifies objects that will be targeted
for overlay of high-resolution videos. It then generates high-
resolution 3D images of historical scenes related to the perspective
of the tourist in real-time. These generated video images are then
overlaid on the view of the real-world as seen by the tourist.
This processing of scenes and generation of high-resolution images is
now discussed in greater detail.
2.1. Processing of Scenes
The task of processing a scene can be broken down into a pipeline of
three consecutive subtasks namely tracking, followed by an
acquisition of a model of the real world, and finally registration
[AUGMENTED].
Tracking: This includes tracking of the three dimensional coordinates
and six dimensional pose (coordinates and orientation) of objects in
the real world [AUGMENTED]. The XR application that runs on the
mobile device needs to track the pose of the user's head, eyes and
the objects that are in view. This requires tracking natural
features that are then used in the next stage of the pipeline.
Acquisition of a model of the real world: The tracked natural
features are used to develop an annotated point cloud (a set of
points in space that are annotated with descriptors) based model that
is then stored in a database. To ensure that this database can be
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scaled up, techniques such as combining a client side simultaneous
tracking and mapping and a server-side localization are used[SLAM_1],
[SLAM_2], [SLAM_3], [SLAM_4].Another model that can be built is based
on polygon mesh and texture mapping technique. The polygon mesh
encodes a 3D object's shape which is expressed as a collection of
small flat surfaces that are polygons. In texture mapping, color
patterns are mapped on to an object's surface. A third modelling
technique uses a 2D lightfield that describes the intensity or color
of the light rays arriving at a single point from arbitrary
directions. Such a 2D lightfield is stored as a two-dimensional
table. Assuming distant light sources, the single point is
approximately valid for small scenes. For larger scenes, many 3D
positions are additionally stored making the table 5D [AUGMENTED].
Registration: The coordinate systems, brightness, and color of
virtual and real objects need to be aligned in a process called
registration [REG]. Once the natural features are tracked as
discussed above, virtual objects are geometrically aligned with those
features by geometric registration. This is followed by resolving
occlusion that can occur between virtual and the real objects
[OCCL_1], [OCCL_2]. The XR application also applies photometric
registration [PHOTO_REG] by aligning the brightness and color between
the virtual and real objects. Additionally, algorithms that
calculate global illumination of both the virtual and real objects
[GLB_ILLUM_1], [GLB_ILLUM_2] are executed. Various algorithms to
deal with artifacts generated by lens distortion [LENS_DIST], blur
[BLUR], noise [NOISE] etc. are also required.
2.2. Generation of Images
The XR application must generate a high-quality video that has the
properties described in the previous step and overlay the video on
the XR device's display- a step called situated visualization. This
entails dealing with registration errors that may arise, ensuring
that there is no visual interference [VIS_INTERFERE], and finally
maintaining temporal coherence by adapting to the movement of user's
eyes and head.
3. Technical Challenges and Solutions
The components of XR applications perform tasks such as real-time
generation and processing of high-quality video content that are
computationally intensive. As a result, on XR devices such as XR
glasses excessive heat is generated by the chip-sets that are
involved in the computation [DEV_HEAT_1], [DEV_HEAT_2].
Additionally, the battery on such devices discharges quickly when
running such applications [BATT_DRAIN].
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A solution to the heat dissipation and battery drainage problem is to
offload the processing and video generation tasks to the remote
cloud. However, running such tasks on the cloud is not feasible as
the end-to-end delays must be within the order of a few milliseconds.
Additionally, such applications require high bandwidth and low jitter
to provide a high QoE to the user. In order to achieve such hard
timing constraints, computationally intensive tasks can be offloaded
to Edge devices.
Another requirement for our use case and similar applications such as
360 degree streaming (streaming of video that represents a view in
every direction in 3D space) is that the display on the XR device
should synchronize the visual input with the way the user is moving
their head. This synchronization is necessary to avoid motion
sickness that results from a time-lag between when the user moves
their head and when the appropriate video scene is rendered. This
time lag is often called "motion-to-photon" delay. Studies have
shown [PER_SENSE], [XR], [OCCL_3] that this delay can be at most 20ms
and preferably between 7-15ms in order to avoid the motion sickness
problem. Out of these 20ms, display techniques including the refresh
rate of write displays and pixel switching take 12-13ms [OCCL_3],
[CLOUD]. This leaves 7-8ms for the processing of motion sensor
inputs, graphic rendering, and round-trip-time (RTT) between the XR
device and the Edge. The use of predictive techniques to mask
latencies has been considered as a mitigating strategy to reduce
motion sickness [PREDICT]. In addition, Edge Devices that are
proximate to the user might be used to offload these computationally
intensive tasks. Towards this end, a 3GPP study indicates an Ultra
Reliable Low Latency of 0.1ms to 1ms for communication between an
Edge server and User Equipment (UE) [URLLC].
Note that the Edge device providing the computation and storage is
itself limited in such resources compared to the Cloud. So, for
example, a sudden surge in demand from a large group of tourists can
overwhelm that device. This will result in a degraded user
experience as their XR device experiences delays in receiving the
video frames. In order to deal with this problem, the client XR
applications will need to use Adaptive Bit Rate (ABR) algorithms that
choose bit-rates policies tailored in a fine-grained manner to the
resource demands and playback the videos with appropriate QoE metrics
as the user moves around with the group of tourists.
However, heavy-tailed nature of several operational parameters make
prediction-based adaptation by ABR algorithms sub-optimal [ABR_2].
This is because with such distributions, law of large numbers works
too slowly [HEAVY_TAIL_2], the mean of sample does not equal the mean
of distribution [HEAVY_TAIL_2], and as a result standard deviation
and variance are unsuitable as metrics for such operational
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parameters [HEAVY_TAIL_1]. Other subtle issues with these
distributions include the "expectation paradox" [HEAVY_TAIL_1] where
the longer the wait for an event, the longer a further need to wait
and the issue of mismatch between the size and count of events
[HEAVY_TAIL_1]. This makes designing an algorithm for adaptation
error-prone and challenging. Such operational parameters include but
are not limited to buffer occupancy, throughput, client-server
latency, and variable transmission times. In addition, edge devices
and communication links may fail and logical communication
relationships between various software components change frequently
as the user moves around with their XR device [UBICOMP].
4. XR Network Traffic
4.1. Traffic Workload
As discussed earlier, the parameters that capture the characteristics
of XR application behavior are heavy tailed. Examples of such
parameters include the distribution of arrival times between XR
application invocation, the amount of data transferred, and the
inter-arrival times of packets within a session. As a result, any
traffic model based on such parameters are themselves heavy-tailed.
Using these models to predict performance under alternative resource
allocations by the network operator is challenging. For example,
both uplink and downlink traffic to a user device has parameters such
as volume of XR data, burst time, and idle time that are heavy
tailed.
Table 1 below shows various XR applications and their associated
throughput requirements [METRICS_1]. Our use case envisages a 6
degrees of freedom (6DoF) video or point cloud and so will require
200 to 1000Mbps of bandwidth. As seen from the table, the XR
application such as our use case transmit a larger amount of data per
unit time as compared to traditional video applications. As a
result, issues arising out of heavy tailed parameters such as long-
range dependent traffic [METRICS_2], self-similar traffic
[METRICS_3], would be experienced at time scales of milliseconds and
microseconds rather than hours or seconds. Additionally, burstiness
at the time scale of tens of milliseconds due to multi-fractal
spectrum of traffic will be experienced [METRICS_4]. Long-range
dependent traffic can have long bursts and various traffic parameters
from widely separated time can show correlation [HEAVY_TAIL_1].
Self-similar traffic contains bursts at a wide range of time scales
[HEAVY_TAIL_1]. Multi-fractal spectrum bursts for traffic summarizes
the statistical distribution of local scaling exponents found in a
traffic trace [HEAVY_TAIL_1]. The operational consequences of XR
traffic having characteristics such as long-range dependency, and
self-similarity is that the edge servers to which multiple XR devices
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are connected wirelessly could face long bursts of traffic
[METRICS_2], [METRICS_3]. In addition, multi-fractal spectrum
burstiness at the scale of milli-seconds could induce jitter
contributing to motion sickness [METRICS_4]. This is because bursty
traffic combined with variable queueing delays leads to large delay
jitter [METRICS_4]. The operators of edge servers will need to run a
'managed edge cloud service' [METRICS_5] to deal with the above
problems. Functionalities that such a managed edge cloud service
could operationally provide include dynamic placement of XR servers,
mobility support and energy management [METRICS_6]. Providing Edge
server support for the techniques being developed at the DETNET
Working Group at the IETF could guarantee performance of XR
applications.
+===================================+=====================+
| Application | Throughput Required |
+===================================+=====================+
| Image and Workflow Downloading | 1 Mbps |
+-----------------------------------+---------------------+
| Video Conferencing | 2 Mbps |
+-----------------------------------+---------------------+
| 3D Model and Data Visualization | 2 to 20 Mbps |
+-----------------------------------+---------------------+
| Two way Telepresence | 5 to 25 Mbps |
+-----------------------------------+---------------------+
| Current-Gen 360 degree video (4K) | 10 to 50 Mbps |
+-----------------------------------+---------------------+
| Next-Gen 360 degree video (8K, | 50 to 200 Mbps |
| 90+ FPS, HDR, Stereoscopic) | |
+-----------------------------------+---------------------+
| 6DoF Video or Point Cloud | 200 to 1000 Mbps |
+-----------------------------------+---------------------+
Table 1: Throughput of some XR Applications
Thus, the provisioning of edge servers in terms of the number of
servers, the topology, where to place them, the assignment of link
capacity, CPUs and GPUs should keep the above factors in mind.
4.2. Traffic Performance Metrics
The performance requirements for XR traffic have characteristics that
need to be considered when operationalizing a network. These
characteristics are now discussed.
The bandwidth requirements of XR applications are substantially
higher than those of video-based applications.
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The latency requirements of XR applications have been studied
recently [XR_TRAFFIC]. The following characteristics were
identified.:
* The uploading of data from an XR device to a remote server for
processing dominates the end-to-end latency.
* A lack of visual features in the grid environment can cause
increased latencies as the XR device uploads additional visual
data for processing to the remote server.
* XR applications tend to have large bursts that are separated by
significant time gaps.
Additionally, XR applications interact with each other on a time
scale of a round-trip-time propagation, and this must be considered
when operationalizing a network.
The following Table 2 [METRICS_6] shows a taxonomy of applications
with their associated required response times and bandwidths.
Response times can be defined as the time interval between the end of
a request submission and the end of the corresponding response from a
system. If the XR device offloads a task to an edge server, the
response time of the server is the round trip time from when a data
packet is sent from the XR device until a response is received. Note
that the required response time provides an upper bound on the sum of
the time taken by computational tasks such as processing of scenes,
generation of images and the round-trip time. This response time
depends only on the Quality of Service (QOS) required by an
application. The response time is therefore independent of the
underlying technology of the network and the time taken by the
computational tasks.
Our use case requires a response time of 20ms at most and preferably
between 7-15ms as discussed earlier. The required bandwidth for our
use case as discussed in section 5.1, Table 1, is 200Mbps-1000Mbps.
Since our use case envisages multiple users running the XR
applications on their devices, and connected to an edge server that
is closest to them, these latency and bandwidth connections will grow
linearly with the number of users. The operators should match the
network provisioning to the maximum number of tourists that can be
supported by a link to an edge server.
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+===================+==============+==========+=====================+
| Application | Required | Expected | Possible |
| | Response | Data | Implementations/ |
| | Time | Capacity | Examples |
+===================+==============+==========+=====================+
| Mobile XR based | Less than 10 | Greater | Assisting |
| remote assistance | milliseconds | than 7.5 | maintenance |
| with uncompressed | | Gbps | technicians, |
| 4K (1920x1080 | | | Industry 4.0 |
| pixels) 120 fps | | | remote |
| HDR 10-bit real- | | | maintenance, |
| time video stream | | | remote assistance |
| | | | in robotics |
| | | | industry |
+-------------------+--------------+----------+---------------------+
| Indoor and | Less than 20 | 50 to | Theme Parks, |
| localized outdoor | milliseconds | 200 Mbps | Shopping Malls, |
| navigation | | | Archaeological |
| | | | Sites, Museum |
| | | | guidance |
+-------------------+--------------+----------+---------------------+
| Cloud-based | Less than 50 | 50 to | Google Live View, |
| Mobile XR | milliseconds | 100 Mbps | XR-enhanced |
| applications | | | Google Translate |
+-------------------+--------------+----------+---------------------+
Table 2: Traffic Performance Metrics of Selected XR Applications
5. IANA Considerations
This document has no IANA actions.
6. Security Considerations
The security issues for the presented use case are similar to other
streaming applications. This document itself introduces no new
security issues.
7. Acknowledgements
Many Thanks to Spencer Dawkins, Rohit Abhishek, Jake Holland, Kiran
Makhijani, Ali Begen, Cullen Jennings, Stephan Wenger, and Eric
Vyncke for providing very helpful feedback suggestions and comments.
8. Informative References
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[ABR_1] Mao, H., Netravali, R., and M. Alizadeh, "Neural Adaptive
Video Streaming with Pensieve", In Proceedings of the
Conference of the ACM Special Interest Group on Data
Communication, pp. 197-210, 2017.
[ABR_2] Yan, F., Ayers, H., Zhu, C., Fouladi, S., Hong, J., Zhang,
K., Levis, P., and K. Winstein, "Learning in situ: a
randomized experiment in video streaming", In 17th USENIX
Symposium on Networked Systems Design and Implementation
(NSDI 20), pp. 495-511, 2020.
[AUGMENTED]
Schmalstieg, D. S. and T.H. Hollerer, "Augmented
Reality", Addison Wesley, 2016.
[AUGMENTED_2]
Azuma, R. T., "A Survey of Augmented
Reality.", Presence:Teleoperators and Virtual
Environments 6.4, pp. 355-385., 1997.
[BATT_DRAIN]
Seneviratne, S., Hu, Y., Nguyen, T., Lan, G., Khalifa, S.,
Thilakarathna, K., Hassan, M., and A. Seneviratne, "A
survey of wearable devices and challenges.", In IEEE
Communication Surveys and Tutorials, 19(4), p.2573-2620.,
2017.
[BLUR] Kan, P. and H. Kaufmann, "Physically-Based Depth of Field
in Augmented Reality.", In Eurographics (Short Papers),
pp. 89-92., 2012.
[CLOUD] Corneo, L., Eder, M., Mohan, N., Zavodovski, A., Bayhan,
S., Wong, W., Gunningberg, P., Kangasharju, J., and J.
Ott, "Surrounded by the Clouds: A Comprehensive Cloud
Reachability Study.", In Proceedings of the Web Conference
2021, pp. 295-304, 2021.
[DEV_HEAT_1]
LiKamWa, R., Wang, Z., Carroll, A., Lin, F., and L. Zhong,
"Draining our Glass: An Energy and Heat characterization
of Google Glass", In Proceedings of 5th Asia-Pacific
Workshop on Systems pp. 1-7, 2013.
[DEV_HEAT_2]
Matsuhashi, K., Kanamoto, T., and A. Kurokawa, "Thermal
model and countermeasures for future smart glasses.",
In Sensors, 20(5), p.1446., 2020.
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[EDGE_1] Satyanarayanan, M., "The Emergence of Edge Computing",
In Computer 50(1) pp. 30-39, 2017.
[EDGE_2] Satyanarayanan, M., Klas, G., Silva, M., and S. Mangiante,
"The Seminal Role of Edge-Native Applications", In IEEE
International Conference on Edge Computing (EDGE) pp.
33-40, 2019.
[EDGE_3] Peterson, L. and O. Sunay, "5G mobile networks: A systems
approach.", In Synthesis Lectures on Network Systems.,
2020.
[GLB_ILLUM_1]
Kan, P. and H. Kaufmann, "Differential irradiance caching
for fast high-quality light transport between virtual and
real worlds.", In IEEE International Symposium on Mixed
and Augmented Reality (ISMAR),pp. 133-141, 2013.
[GLB_ILLUM_2]
Franke, T., "Delta voxel cone tracing.", In IEEE
International Symposium on Mixed and Augmented Reality
(ISMAR), pp. 39-44, 2014.
[HEAVY_TAIL_1]
Crovella, M. and B. Krishnamurthy, "Internet measurement:
infrastructure, traffic and applications", John Wiley and
Sons Inc., 2006.
[HEAVY_TAIL_2]
Taleb, N., "The Statistical Consequences of Fat Tails",
STEM Academic Press, 2020.
[HEAVY_TAIL_3]
Ehrenberg, A., "A Primer in Data Reduction.", John Wiley,
London, 1982.
[LENS_DIST]
Fuhrmann, A. and D. Schmalstieg, "Practical calibration
procedures for augmented reality.", In Virtual
Environments 2000, pp. 3-12. Springer, Vienna, 2000.
[METRICS_1]
ABI Research, "Augmented and Virtual Reality: The first
Wave of Killer Apps.",
https://gsacom.com/paper/augmented-virtual-reality-first-
wave-5g-killer-apps-qualcomm-abi-research/, 2017.
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[METRICS_2]
Paxon, V. and S. Floyd, "Wide Area Traffic: The Failure of
Poisson Modelling.", In IEEE/ACM Transactions on
Networking, pp. 226-244., 1995.
[METRICS_3]
Willinger, W., Taqqu, M.S., Sherman, R., and D.V. Wilson,
"Self-Similarity Through High Variability: Statistical
Analysis and Ethernet LAN Traffic at Source Level.",
In IEEE/ACM Transactions on Networking, pp. 71-86., 1997.
[METRICS_4]
Gilbert, A.C., "Multiscale Analysis and Data Networks.",
In Applied and Computational Harmonic Analysis, pp.
185-202., 2001.
[METRICS_5]
Beyer, B., Jones, C., Petoff, J., and N.R. Murphy, "Site
Reliability Engineering: How Google Runs Production
Systems.", O'Reilly Media, Inc., 2016.
[METRICS_6]
Siriwardhana, Y., Porambage, P., Liyanage, M., and M.
Ylianttila, "A survey on mobile augmented reality with 5G
mobile edge computing: architectures, applications, and
technical aspects.", In IEEE Communications Surveys and
Tutorials, Vol 23, No. 2, 2021.
[NOISE] Fischer, J., Bartz, D., and W. Straßer, "Enhanced visual
realism by incorporating camera image effects.",
In IEEE/ACM International Symposium on Mixed and Augmented
Reality, pp. 205-208., 2006.
[OCCL_1] Breen, D.E., Whitaker, R.T., and M. Tuceryan, "Interactive
Occlusion and automatic object placementfor augmented
reality", In Computer Graphics Forum, vol. 15, no. 3 , pp.
229-238,Edinburgh, UK: Blackwell Science Ltd, 1996.
[OCCL_2] Zheng, F., Schmalstieg, D., and G. Welch, "Pixel-wise
closed-loop registration in video-based augmented
reality", In IEEE International Symposium on Mixed and
Augmented Reality (ISMAR), pp. 135-143, 2014.
[OCCL_3] Lang, B., "Oculus Shares 5 Key Ingredients for Presence in
Virtual Reality.", https://www.roadtovr.com/oculus-
shares-5-key-ingredients-for-presence-in-virtual-reality/,
2014.
Krishna & Rahman Expires 5 September 2024 [Page 13]
Internet-Draft MOPS AR Use Case March 2024
[PER_SENSE]
Mania, K., Adelstein, B.D., Ellis, S.R., and M.I. Hill,
"Perceptual sensitivity to head tracking latency in
virtual environments with varying degrees of scene
complexity.", In Proceedings of the 1st Symposium on
Applied perception in graphics and visualization pp.
39-47., 2004.
[PHOTO_REG]
Liu, Y. and X. Granier, "Online tracking of outdoor
lighting variations for augmented reality with moving
cameras", In IEEE Transactions on visualization and
computer graphics, 18(4), pp.573-580, 2012.
[PREDICT] Buker, T. J., Vincenzi, D.A., and J.E. Deaton, "The effect
of apparent latency on simulator sickness while using a
see-through helmet-mounted display: Reducing apparent
latency with predictive compensation..", In Human factors
54.2, pp. 235-249., 2012.
[REG] Holloway, R. L., "Registration error analysis for
augmented reality.", In Presence:Teleoperators and Virtual
Environments 6.4, pp. 413-432., 1997.
[SLAM_1] Ventura, J., Arth, C., Reitmayr, G., and D. Schmalstieg,
"A minimal solution to the generalized pose-and-scale
problem", In Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pp. 422-429,
2014.
[SLAM_2] Sweeny, C., Fragoso, V., Hollerer, T., and M. Turk, "A
scalable solution to the generalized pose and scale
problem", In European Conference on Computer Vision, pp.
16-31, 2014.
[SLAM_3] Gauglitz, S., Sweeny, C., Ventura, J., Turk, M., and T.
Hollerer, "Model estimation and selection towards
unconstrained real-time tracking and mapping", In IEEE
transactions on visualization and computer graphics,
20(6), pp. 825-838, 2013.
[SLAM_4] Pirchheim, C., Schmalstieg, D., and G. Reitmayr, "Handling
pure camera rotation in keyframe-based SLAM", In 2013 IEEE
international symposium on mixed and augmented reality
(ISMAR), pp. 229-238, 2013.
Krishna & Rahman Expires 5 September 2024 [Page 14]
Internet-Draft MOPS AR Use Case March 2024
[UBICOMP] Bardram, J. and A. Friday, "Ubiquitous Computing Systems",
In Ubiquitous Computing Fundamentals pp. 37-94. CRC Press,
2009.
[URLLC] 3GPP, "3GPP TR 23.725: Study on enhancement of Ultra-
Reliable Low-Latency Communication (URLLC) support in the
5G Core network (5GC).",
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3453, 2019.
[VIS_INTERFERE]
Kalkofen, D., Mendez, E., and D. Schmalstieg, "Interactive
focus and context visualization for augmented reality.",
In 6th IEEE and ACM International Symposium on Mixed and
Augmented Reality, pp. 191-201., 2007.
[XR] 3GPP, "3GPP TR 26.928: Extended Reality (XR) in 5G.",
https://portal.3gpp.org/desktopmodules/Specifications/
SpecificationDetails.aspx?specificationId=3534, 2020.
[XR_TRAFFIC]
Apicharttrisorn, K., Balasubramanian, B., Chen, J.,
Sivaraj, R., Tsai, Y., Jana, R., Krishnamurthy, S., Tran,
T., and Y. Zhou, "Characterization of Multi-User Augmented
Reality over Cellular Networks", In 17th Annual IEEE
International Conference on Sensing, Communication, and
Networking (SECON), pp. 1-9. IEEE, 2020.
Authors' Addresses
Renan Krishna
InterDigital Europe Limited
64, Great Eastern Street
London
EC2A 3QR
United Kingdom
Email: renan.krishna@interdigital.com
Akbar Rahman
Ericsson
349 Terry Fox Drive
Ottawa Ontario K2K 2V6
Canada
Email: Akbar.Rahman@ericsson.com
Krishna & Rahman Expires 5 September 2024 [Page 15]
