Internet DRAFT - draft-zhu-rmcat-video-traffic-source
draft-zhu-rmcat-video-traffic-source
Network Working Group X. Zhu
Internet-Draft S. Mena
Intended status: Informational Cisco Systems
Expires: January 4, 2016 Z. Sarker
Ericsson AB
July 3, 2015
Modeling Video Traffic Sources for RMCAT Evaluations
draft-zhu-rmcat-video-traffic-source-02
Abstract
This document describes two reference video traffic source models for
evaluating RMCAT candidate algorithms. The first model statistically
characterizes the behavior of a live video encoder in response to
changing requests on target video rate. The second model is trace-
driven, and emulates the encoder output by scaling the pre-encoded
video frame sizes from a widely used video test sequence. Both
models are designed to strike a balance between simplicity,
repeatability, and authenticity in modeling the interactions between
a video traffic source and the congestion control module.
Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Desired Behavior of A Synthetic Video Traffic Model . . . . . 3
4. Interactions Between Synthetic Video Traffic Source and
Other Components at the Sender . . . . . . . . . . . . . . . 4
5. A Statistical Reference Model . . . . . . . . . . . . . . . . 6
5.1. Time-damped response to target rate update . . . . . . . 7
5.2. Temporary burst/oscillation during transient . . . . . . 7
5.3. Output rate fluctuation at steady state . . . . . . . . . 8
5.4. Rate range limit imposed by video content . . . . . . . . 8
6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 8
6.1. Choosing the video sequence and generating the traces . . 9
6.2. Using the traces in the syntethic codec . . . . . . . . . 10
6.2.1. Main algorithm . . . . . . . . . . . . . . . . . . . 10
6.2.2. Notes to the main algorithm . . . . . . . . . . . . . 12
6.3. Varying frame rate and resolution . . . . . . . . . . . . 12
7. Comparing and Combining The Two Models . . . . . . . . . . . 13
8. Implementation Status . . . . . . . . . . . . . . . . . . . . 14
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 14
10. References . . . . . . . . . . . . . . . . . . . . . . . . . 14
10.1. Normative References . . . . . . . . . . . . . . . . . . 14
10.2. Informative References . . . . . . . . . . . . . . . . . 14
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 15
1. Introduction
When evaluating candidate congestion control algorithms designed for
real-time interactive media, it is important to account for the
characteristics of traffic patterns generated from a live video
encoder. Unlike synthetic traffic sources that can conform perfectly
to the rate changing requests from the congestion control module, a
live video encoder can be sluggish in reacting to such changes.
Output rate of a live video encoder also typically deviates from the
target rate due to uncertainties in the encoder rate control process.
Consequently, end-to-end delay and loss performance of a real-time
media flow can be further impacted by rate variations introduced by
the live encoder.
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On the other hand, evaluation results of a candidate RMCAT algorithm
should mostly reflect performance of the congestion control module,
and somewhat decouple from pecularities of any specific video codec.
It is also desirable that evaluation tests are repeatable, and be
easily duplicated across different candidate algorithms.
One way to strike a balance between the above considerations is to
evaluate RMCAT algorithms using a synthetic video traffic source
model that captures key characteristics of the behavior of a live
video encoder. To this end, this draft presents two reference
models. The first is based on statistical modelling; the second is
trace-driven. The draft also discusses the pros and cons of each
approach, as well as the possibility to combine both.
2. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described RFC2119 [RFC2119].
3. Desired Behavior of A Synthetic Video Traffic Model
A live video encoder employs encoder rate control to meet a target
rate by varying its encoding parameters, such as quantization step
size, frame rate, and picture resolution, based on its estimate of
the video content (e.g., motion and scene complexity). In practice,
however, several factors prevent the output video rate from perfectly
conforming to the input target rate.
Due to uncertainties in the captured video scene, the output rate
typically deviates from the specified target. In the presence of a
significant change in target rate, it sometimes takes several frames
before the encoder output rate converges to the new target. Finally,
while most of the frames in a live session are encoded in predictive
mode, the encoder can occasionally generate a large intra-coded frame
(or a frame partially containing intra-coded blocks) in an attempt to
recover from losses, to re-sync with the receiver, or during the
transient period of responding to target rate or spatial resolution
changes.
Hence, a synthetic video source should have the following
capabilities:
o To change bitrate. This includes ability to change framerate and/
or spatial resolution, or to skip frames when required.
o To fluctuate around the target bitrate specified by the congestion
control module.
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o To delay in convergence to the target bitrate.
o To generate intra-coded or repair frames on demand.
While there exists many different approaches in developing a
synthetic video traffic model, it is desirable that the outcome
follows a few common characteristics, as outlined below.
o Low computational complexity: The model should be computationally
lightweight, otherwise it defeats the whole purpose of serving as
a substitute for a live video encoder.
o Temporal pattern similarity: The individual traffic trace
instances generated by the model should mimic the temporal pattern
of those from a real video encoder.
o Statistical resemblance: The synthetic traffic should match the
outcome of the real video encoder in terms of statistical
characteristics, such as the mean, variance, peak, and
autocorrelation coefficients of the bitrate. It is also important
that the statistical resemblance should hold across different time
scales, ranging from tens of milliseconds to sub-seconds.
o Wide range of coverage: The model should be easily configurable to
cover a wide range of codec behaviors (e.g., with either fast or
slow reaction time in live encoder rate control) and video content
variations (e.g, ranging from high-motion to low-motion).
These distinct behavior features can be characterized via simple
statistical models, or a trace-driven approach. We present an
example of each in Section 5 and Section 6
4. Interactions Between Synthetic Video Traffic Source and Other
Components at the Sender
Figure 1 depitcs the interactions of the synthetic video encoder with
other components at the sender, such as the application, the
congestion control module, the media packet transport module, etc.
Both reference models, as described later in Section 5 and Section 6,
follow the same set of interactions.
The synthetic video encoder takes in raw video frames captured by the
camera and then dynamically generates a sequence of encoded video
frames with varying size and interval. These encoded frames are
processed by other modules in order to transmit the video stream over
the network. During the lifetime of a video transmission session,
the synthetic video encoder will typically be required to adapt its
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encoding bitrate, and sometimes the spatial resolution and frame
rate.
In our model, the synthetic video encoder module has group of
incoming and outgoing interface calls that allow for interaction with
other modules. The following are some of the possible incoming
interface calls --- marked as (a) in Figure 1 --- that the synthetic
video encoder may accept. The list is not exhaustive and can be
complemented by other interface calls if deemed necessary.
o Target rate R_v(t): requested at time t, typically from the
congestion control module. Depending on the congestion control
algorithm in use, the update requests can either be periodic
(e.g., once per 1 second), or on-demand (e.g., only when a drastic
bandwidth change over the network is observed).
o Target frame rate FPS(t): the instantaneous frame rate measured in
frames-per-second at time t. This depends on the native camera
capture frame rate as well as the target/preferred frame rate
configured by the application or user.
o Frame resolution XY(t): the 2-dimensional vector indicating the
preferred frame resolution in pixels at time t. Several factors
govern the resolution requested to the synthetic video encoder
over time. Examples of such factors are the capturing resolution
of the native camera; or the current target rate R_v(t), since
very small resolutions do not make sense with very high bitrates,
and vice-versa.
o Instant frame skipping: the request to skip the encoding of one or
several captured video frames, for instance when a drastic
decrease in available network bandwidth is detected.
o On-demand generation of intra (I) frame: the request to encode
another I frame to avoid further error propagation at the
receiver, if severe packet losses are observed. This request
typically comes from the error control module.
An example of outgoing interface call --- marked as (b) in Figure 1
--- is the rate range, that is, the dynamic range of the video
encoder's output rate for the current video contents: [R_min, R_max].
Here, R_min and R_max are meant to capture the dynamic rate range the
encoder is capable of outputting. This typically depends on the
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video content complexity and/or display type (e.g., higher R_max for
video contents with higher motion complexity, or for displays of
higher resolution). Therefore, these values will not change with
R_v, but may change over time if the content is changing.
+-------------+
raw video | | encoded video
frames | Synthetic | frames
------------> | Video | -------------->
| Encoder |
| |
+--------+----+
/|\ |
| |
-------------------+ +-------------------->
interface from interface to
other modules (a) other modules (b)
Figure 1: Interaction between synthetic video encoder, congestion
control, and packet transport module.
5. A Statistical Reference Model
In this section, we describe one simple statistical model of the live
video encoder traffic source. Figure 2 summarizes the list of tuable
parameters in this statistical model. A more comprehensive survey of
popular methods for modelling video traffic source behavior can be
found in [Tanwir2013].
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+---------------+--------------------------------+----------------+
| Notation | Parameter Name | Example Value |
+--------------+---------------------------------+----------------+
| R_v(t) | Target rate request at time t | 1 Mbps |
| R_o(t) | Output rate at time t | 1.2 Mbps |
| tau_v | Encoder reaction latency | 0.2 s |
| K_d | Burst duration during transient | 5 frames |
| K_r | Burst size dur transient | 5:1 |
| R_e(t) | Error in output rate at time t | 0.2 Mbps |
| SIGMA | standard deviation of normally | 0.1 |
| | distributed relative rate error | |
| DELTA | upper and lower bound (+/-) of | 0.1 |
| | uniformally distributed relative| |
| | rate error | |
| R_min | minimum rate supported by video | 150 Kbps |
| | encoder or content activity | |
| R_max | maximum rate supported by video | 1.5Mbps |
| | encoder or content activity | |
+--------------+---------------------------------+----------------+
Figure 2: List of tunable parameters in a statistical video traffic
source model.
5.1. Time-damped response to target rate update
While the congestion control module can update its target rate
request R_v(t) at any time, our model dictates that the encoder will
only react to such changes after tau_v seconds from a previous rate
transition. In other words, when the encoder has reacted to a rate
change request at time t, it will simply ignore all subsequent rate
change requests until time t+tau_v.
5.2. Temporary burst/oscillation during transient
The output rate R_o during the period [t, t+tau_v] is considered to
be in transient. Based on observations from video encoder output
data, we model the transient behavior of an encoder upon reacting to
a new target rate request in the form of largely varying output
sizes. It is assumed that the overall average output rate R_o during
this period matches the target rate R_v. Consequently, the
occasional burst of large frames are followed by smaller-than average
encoded frames.
This temporary burst is characterized by two parameters:
o burst duration K_d: number frames in the burst event; and
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o burst size K_r: ratio of a burst frame and average frame size at
steady state.
It can be noted that these burst parameters can also be used to mimic
the insersion of a large on-demand I frame in the presence of severe
packet losses. The values of K_d and K_r are fitted to reflect the
typical ratio between I and P frames for a given video content.
5.3. Output rate fluctuation at steady state
We model output rate R_o as randomly fluctuating around the target
rate R_v after convergence. There are two variants in modeling the
random fluctuation R_e = R_o - R_v:
o As normal distribution: with a mean of zero and a standard
deviation SIGMA specified in terms of percentage of the target
rate. A typical value of SIGMA is 10 percent of target rate.
o As uniform distribution bounded between -DELTA and DELTA. A
typical value of DELTA is 10 percent of target rate.
The distribution type (normal or uniform) and model parameters (SIGMA
or DELTA) can be learned from data samples gathered from a live
encoder output.
5.4. Rate range limit imposed by video content
The output rate R_o is further clipped within the dynamic range
[R_min, R_max], which in reality are dictated by scene and motion
complexity of the captured video content. In our model, these
parameters are specified by the application.
6. A Trace-Driven Model
We now present the second approach to model a video traffic source.
This approach is based on running an actual live video encoder
offline on a set of chosen raw video sequences and using the
encoder's output traces for constructing a synthetic live encoder.
With this approach, the recorded video traces naturally exhibit
temporal fluctuations around a given target rate request R_v(t) from
the congestion control module.
The following list summarizes this approach's main steps:
1) Choose one or more representative raw video sequences.
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2) Using an actual live video encoder, encode the sequences at
various bitrates. Keep just the sequences of frame sizes for each
bitrate.
3) Construct a data structure that contains the output of the
previous step. The data structure should allow for easy bitrate
lookup.
4) Upon a target bitrate request R_v(t) from the controller, look up
the closest bitrates among those previously stored. Use the frame
size sequences stored for those bitrates to approximate the frame
sizes to output.
5) The output of the synthetic encoder contains "encoded" frames with
random contents but with realistic sizes.
Section 6.1 explains steps 1), 2), and 3), Section 6.2 elaborates on
steps 4) and 5). Finally, Section 6.3 briefly discusses the
possibility to extend the model for supporting variable frame rate
and/or variable frame resolution.
6.1. Choosing the video sequence and generating the traces
The first step we need to perform is a careful choice of a set of
video sequences that are representative of the use cases we want to
model. Our use case here is video conferencing, so we must choose a
low-motion sequence that resembles a "talking head", for instance a
news broadcast or a video capture of an actual conference call.
The length of the chosen video sequence is a tradeoff. If it is too
long, it will be difficult to manage the data structures containing
the traces we will produce in the next steps. If it is too short,
there will be an obvious periodic pattern in the output frame sizes,
leading to biased results when evaluating congestion controller
performance. In our experience, a one-minute-long sequence is a fair
tradeoff.
Once we have chosen the raw video sequence, denoted S, we use a live
encoder, e.g. [H264] or [HEVC] to produce a set of encoded
sequences. As discussed in Section 3, a live encoder's output
bitrate can be tuned by varying three input parameters, namely,
quantization step size, frame rate, and picture resolution. In order
to simplify the choice of these parameters for a given target rate,
we assume a fixed frame rate (e.g. 25 fps) and a fixed resolution
(e.g., 480p). See section 6.3 for a discussion on how to relax these
assumptions.
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Following these simplifications, we run the chosen encoder by setting
a constant target bitrate at the beginning, then letting the encoder
vary the quantization step size internally while encoding the input
video sequence. Besides, we assume that the first frame is encoded
as an I-frame and the rest are P-frames. We further assume that the
encoder algorithm does not use knowledge of frames in the future so
as to encode a given frame.
We define R_min and R_max as the minimum and maximum bitrate at which
the synthetic codec is to operate. We divide the bitrate range
between R_min and R_max in n_s + 1 bitrate steps of length l = (R_max
- R_min) / n_s. We then use the following simple algorithm to encode
the raw video sequence.
r = R_min
while r <= R_max do
Traces[r] = encode_sequence(S, r, e)
r = r + l
where function encode_sequence takes as parameters, respectively, a
raw video sequence, a constant target rate, and an encoder algorithm;
it returns a vector with the sizes of frames in the order they were
encoded. The output vector is stored in a map structure called
Traces, whose keys are bitrates and values are frame size vectors.
The choice of a value for n_s is important, as it determines the
number of frame size vectors stored in map Traces. The minimum value
one can choose for n_s is 1, and its maximum value depends on the
amount of memory available for holding map Traces. A reasonable
value for n_s is one that makes the steps' length l = 200 kbps. We
will further discuss step length l in the next section.
6.2. Using the traces in the syntethic codec
The main idea behind the trace-based synthetic codec is that it
mimics a real live codec's rate adaptation when the congestion
controller updates the target rate R_v(t). It does so by switching
to a different frame size vector stored in the map Traces when
needed.
6.2.1. Main algorithm
We maintain two variables r_current and t_current:
* r_current points to one of the keys of the map Traces. Upon a
change in the value of R_v(t), typically because the congestion
controller detects that the network conditions have changed,
r_current is updated to the greatest key in Traces that is less than
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or equal to the new value of R_v(t). For the moment, we assume the
value of R_v(t) to be clipped in the range [R_min, R_max].
r_current = r
such that
( r in keys(Traces) and
r <= R_v(t) and
(not(exists) r' in keys(Traces) such that r < r' <= R_v(t)) )
* t_current is an index to the frame size vector stored in
Traces[r_current]. It is updated every time a new frame is due. We
assume all vectors stored in Traces to have the same size, denoted
size_traces. The following equation governs the update of t_current:
if t_current < SkipFrames then
t_current = t_current + 1
else
t_current = ((t_current+1-SkipFrames) % (size_traces- SkipFrames))
+ SkipFrames
where operator % denotes modulo, and SkipFrames is a predefined
constant that denotes the number of frames to be skipped at the
beginning of frame size vectors after t_current has wrapped around.
The point of constant SkipFrames is avoiding the effect of
periodically sending a (big) I-frame followed by several smaller-
than-normal P-frames. We typically set SkipFrames to 20, although it
could be set to 0 if we are interested in studying the effect of
sending I-frames periodically.
We initialize r_current to R_min, and t_current to 0.
When a new frame is due, we need to calculate its size. There are
three cases:
a) R_min <= R_v(t) < Rmax: In this case we use linear interpolation
of the frame sizes appearing in Traces[r_current] and
Traces[r_current + l]. The interpolation is done as follows:
size_lo = Traces[r_current][t_current]
size_hi = Traces[r_current + l][t_current]
distance_lo = ( R_v(t) - r_current ) / l
framesize = size_hi * distance_lo + size_lo * (1 - distance_lo)
b) R_v(t) < R_min: In this case, we scale the trace sequence with
the lowest bitrate, in the following way:
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factor = R_v(t) / R_min
framesize = max(1, factor * Traces[R_min][t_current])
c) R_v(t) >= R_max: We also use scaling for this case. We use the
trace sequence with the greatest bitrate:
factor = R_v(t) / R_max
framesize = factor * Traces[R_max][t_current]
In case b), we set the minimum to 1 byte, since the value of factor
can be arbitrarily close to 0.
6.2.2. Notes to the main algorithm
* Reacting to changes in target bitrate. Similarly to the
statistical model presented in Section 5, the trace-based synthetic
codec has a time bound, tau_v, to reacting to target bitrate changes.
If the codec has reacted to an update in R_v(t) at time t, it will
delay any further update to R_v(t) to time t + tau_v. Note that, in
any case, the value of tau_v cannot be chosen shorter than the time
between frames, i.e. the inverse of the frame rate.
* I-frames on demand. The synthetic codec could be extended to
simulate the sending of I-frames on demand, e.g., as a reaction to
losses. To implement this extension, the codec's API is augmented
with a new function to request a new I-frame. Upon calling such
function, t_current is reset to 0.
* Variable length l of steps defined between R_min and R_max. In the
main algorithm's description, the step length l is fixed. However,
if the range [R_min, R_max] is very wide, it is also possible to
define a set of steps with a non-constant length. The idea behind
this modification is that the difference between 400 kbps and 600
kbps as bitrate is much more important than the difference between
4400 kbps and 4600 kbps. For example, one could define steps of
length 200 Kbps under 1 Mbps, then length 300 kbps between 1 Mbps and
2 Mbps, 400 kbps between 2 Mbps and 3 Mbps, and so on.
6.3. Varying frame rate and resolution
The trace-based synthetic codec model explained in this section is
relatively simple because we have fixed the frame rate and the frame
resolution. The model could be extended to have variable frame rate,
variable spatial resolution, or both.
When the encoded picture quality at a given bitrate is low, one can
potentially decrease the frame rate (if the video sequence is
currently in low motion) or the spatial resolution in order to
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improve quality-of-experince (QoE) in the overall encoded video. On
the other hand, if target bitrate increases to a point where there is
no longer a perceptible improvement in the picture quality of
individual frames, then one might afford to increase the spatial
resolution or the frame rate (useful if the video is currently in
high motion).
Many techniques have been proposed to choose over time the best
combination of encoder quatization step size, frame rate, and spatial
resolution in order to maximize the quality of live video codecs
[Ozer2011][Hu2010]. Future work may consider extending the trace-
based codec to accommodate variable frame rate and/or resolution.
From the perspective of congestion control, varying the spatial
resolution typically requires a new intra-coded frame to be
generated, thereby incurring a temporary burst in the output traffic
pattern. The impact of frame rate change tends to be more subtle:
reducing frame rate from high to low leads to sparsely spaced larger
encoded packets instead of many densely spaced smaller packets. Such
difference in traffic profiles may still affect the performance of
congestion control, especially when outgoing packets are not paced at
the transport module. We leave the investigation of varying frame
rate to future work.
7. Comparing and Combining The Two Models
It is worthwhile noting that the statistical and trace-based models
each has its own advantages and drawbacks. Both models are fairly
simple to implement. However, it takes significantly more effort to
fit the parameters of a statistical model to actual encoder output
data whereas trace-based models does not require such fitting. On
the other hand, once validated, the statistical model is more
flexible in mimicking a wide range of encoder/content behavior by
simply varying the correponding parameters in the model. In
contrast, a trace-driven model relies, by definition, on additional
data collection efforts for accommodating new codecs or video
contents.
In general, trace-based model is more realistic for mimicking
ongoing, steady-state behavior of a video traffic source whereas
statistical model is more versatile for simulating transient events
(e.g., when target rate changes from A to B with temporary bursts
during the transition). Therefore, it may be desirable to combine
both approaches into a hybrid model, using traces for steady-state
and statistical model for transients.
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8. Implementation Status
The statistical model has been implemented as a traffic generator
module within the [ns-2] network simulation platform. The trace-
driven model has been implemented as a stand-alone traffic source
module which can be easily integrated into the [ns-3] network
simulation platform.
Authors of this draft are currently in the process of providing open
source access to both implementations.
9. IANA Considerations
There are no IANA impacts in this memo.
10. References
10.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, March 1997.
[H264] ITU-T Recommendation H.264, "Advanced video coding for
generic audiovisual services",
<http://www.itu.int/rec/T-REC-H.264-201304-I>.
[HEVC] ITU-T Recommendation H.265, "High efficiency video
coding", .
10.2. Informative References
[Hu2010] Hu, H., Ma, Z., and Y. Wang, "Optimization of Spatial,
Temporal and Amplitude Resolution for Rate-Constrained
Video Coding and Scalable Video Adaptation", inproceedings
in Proc. 19th IEEE International Conference on Image
Processing (ICIP'12), September 2012.
[Ozer2011]
Ozer, J., "Video Compression for Flash, Apple Devices and
HTML5", ISBN ISBN-13:978-0976259503, 2011.
[Tanwir2013]
Tanwir, S. and H. Perros, "A Survey of VBR Video Traffic
Models", journal IEEE Communications Surveys and
Tutorials, vol. 15, no. 5, pp. 1778-1802., October 2013.
[ns-2] "The Network Simulator - ns-2",
<http://www.isi.edu/nsnam/ns/>.
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[ns-3] "The Network Simulator - ns-3", <https://www.nsnam.org/>.
Authors' Addresses
Xiaoqing Zhu
Cisco Systems
12515 Research Blvd., Building 4
Austin, TX 78759
USA
Email: xiaoqzhu@cisco.com
Sergio Mena de la Cruz
Cisco Systems
EPFL, Quartier de l'Innovation, Batiment E
Ecublens, Vaud 1015
Switzerland
Email: semena@cisco.com
Zaheduzzaman Sarker
Ericsson AB
Luleae, SE 977 53
Sweden
Phone: +46 10 717 37 43
Email: zaheduzzaman.sarker@ericsson.com
Zhu, et al. Expires January 4, 2016 [Page 15]
