Internet Engineering Task Force (IETF) X. Zhu
Request for Comments: 8593 S. Mena
Category: Informational Cisco Systems
ISSN: 2070-1721 Z. Sarker
Ericsson AB
May 2019
Video Traffic Models for RTP Congestion Control Evaluations
Abstract
This document describes two reference video traffic models for
evaluating RTP congestion control algorithms. The first model
statistically characterizes the behavior of a live video encoder in
response to changing requests on the target video rate. The second
model is trace-driven and emulates the output of actual encoded video
frame sizes from a high-resolution test sequence. Both models are
designed to strike a balance between simplicity, repeatability, and
authenticity in modeling the interactions between a live video
traffic source and the congestion control module. Finally, the
document describes how both approaches can be combined into a hybrid
model.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/rfc8593.
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Copyright Notice
Copyright (c) 2019 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
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the Trust Legal Provisions and are provided without warranty as
described in the Simplified BSD License.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 3
3. Desired Behavior of a Synthetic Video Traffic Model . . . . . 4
4. Interactions between Synthetic Video Traffic Source and
Other Components at the Sender . . . . . . . . . . . . . . . 5
5. A Statistical Reference Model . . . . . . . . . . . . . . . . 7
5.1. Time-Damped Response to Target-Rate Update . . . . . . . 9
5.2. Temporary Burst and Oscillation during the Transient
Period . . . . . . . . . . . . . . . . . . . . . . . . . 9
5.3. Output-Rate Fluctuation at Steady State . . . . . . . . . 9
5.4. Rate Range Limit Imposed by Video Content . . . . . . . . 10
6. A Trace-Driven Model . . . . . . . . . . . . . . . . . . . . 10
6.1. Choosing the Video Sequence and Generating the Traces . . 11
6.2. Using the Traces in the Synthetic Codec . . . . . . . . . 13
6.2.1. Main Algorithm . . . . . . . . . . . . . . . . . . . 13
6.2.2. Notes to the Main Algorithm . . . . . . . . . . . . . 14
6.3. Varying Frame Rate and Resolution . . . . . . . . . . . . 15
7. Combining the Two Models . . . . . . . . . . . . . . . . . . 16
8. Reference Implementation . . . . . . . . . . . . . . . . . . 17
9. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 17
10. Security Considerations . . . . . . . . . . . . . . . . . . . 17
11. References . . . . . . . . . . . . . . . . . . . . . . . . . 17
11.1. Normative References . . . . . . . . . . . . . . . . . . 17
11.2. Informative References . . . . . . . . . . . . . . . . . 18
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 19
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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. The
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.
On the other hand, evaluation results of a candidate RTP congestion
control algorithm should mostly reflect the performance of the
congestion control module and somewhat decouple from peculiarities of
any specific video codec. It is also desirable that evaluation tests
are repeatable and easily duplicated across different candidate
algorithms.
One way to strike a balance between the above considerations is to
evaluate congestion control algorithms using a synthetic video
traffic source model that captures key characteristics of the
behavior of a live video encoder. The synthetic traffic model should
also contain tunable parameters so that it can be flexibly adjusted
to reflect the wide variations in real-world live video encoder
behaviors. To this end, this document presents two reference models.
The first is based on statistical modeling. The second is driven by
frame size and interval traces recorded from a real-world encoder.
This document also discusses the pros and cons of each approach, as
well as how both approaches can be combined into a hybrid model.
2. Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
"OPTIONAL" in this document are to be interpreted as described in
BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
capitals, as shown here.
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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, the encoder's output frame sizes
sometimes fluctuate for a short, transient period of time before the
output rate converges to the new target. Finally, while most of the
frames in a live session are encoded in predictive mode (i.e.,
P-frames in [H264]), the encoder can occasionally generate a large
intra-coded frame (i.e., I-frame as defined in [H264]) 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 the ability to change frame rate
and/or spatial resolution or to skip frames upon request.
o To fluctuate around the target bitrate specified by the congestion
control module.
o To show a delay in convergence to the target bitrate.
o To generate intra-coded or repair frames on demand.
While there exist 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.
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o Statistical resemblance: The synthetic traffic source 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 A 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 to low motion).
These distinct behavior features can be characterized via simple
statistical modeling or a trace-driven approach. Sections 5 and 6
provide an example of each approach, respectively. Section 7
discusses how both models can be combined together.
4. Interactions between Synthetic Video Traffic Source and Other
Components at the Sender
Figure 1 depicts the interactions of the synthetic video traffic
source 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 Sections 5 and 6,
follow the same set of interactions.
The synthetic video source dynamically generates a sequence of dummy
video frames with varying size and interval. These dummy 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 source will typically be required to adapt its
encoding bitrate and sometimes the spatial resolution and frame rate.
In this model, the synthetic video source module has a 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
traffic source may accept. The list is not exhaustive and can be
complemented by other interface calls if necessary.
o Target bitrate R_v: Target bitrate request measured in bits per
second (bps). Typically, the congestion control module calculates
the target bitrate and updates it dynamically over time.
Depending on the congestion control algorithm in use, the update
requests can either be periodic (e.g., once per second), or
on-demand (e.g., only when a drastic bandwidth change over the
network is observed).
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o Target frame rate FPS: The instantaneous frame rate measured in
frames per second at a given time. This depends on the native
camera-capture frame rate as well as the target/preferred frame
rate configured by the application or user.
o Target frame resolution XY: The 2-dimensional vector indicating
the preferred frame resolution in pixels. Several factors govern
the resolution requested to the synthetic video source over time.
Examples of such factors include the capturing resolution of the
native camera and the display size of the destination screen. The
target frame resolution also depends on the current target bitrate
R_v, since it does not make sense to pair very low spatial
resolutions 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
when severe packet losses are observed. This request typically
comes from the error control module. It can be initiated either
by the sender or by the receiver via Full Intra Request (FIR)
messages as defined in [RFC5104].
An example of an outgoing interface call, marked as (b) in Figure 1,
is the rate range [R_min, R_max]. Here, R_min and R_max are meant to
capture the dynamic rate range the actual live video encoder is
capable of generating given the input video content. This typically
depends on the video content complexity and/or display type (e.g.,
higher R_max for video content 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.
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+-------------+
| | dummy encoded
| Synthetic | video frames
| Video | -------------->
| Source |
| |
+--------+----+
/|\ |
| |
-------------------+ +-------------------->
interface from interface to
other modules (a) other modules (b)
Figure 1: Interaction between Synthetic Video Encoder
and Other Modules at the Sender
5. A Statistical Reference Model
This section describes one simple statistical model of the live video
traffic source. Figure 2 summarizes the list of tunable parameters
in this statistical model. A more comprehensive survey of popular
methods for modeling the behavior of video traffic sources can be
found in [Tanwir2013].
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+===========+====================================+================+
| Notation | Parameter Name | Example Value |
+===========+====================================+================+
| R_v | Target bitrate request | 1 Mbps |
+-----------+------------------------------------+----------------+
| FPS | Target frame rate | 30 Hz |
+-----------+------------------------------------+----------------+
| tau_v | Encoder reaction latency | 0.2 s |
+-----------+------------------------------------+----------------+
| K_d | Burst duration of the transient | 8 frames |
| | period | |
+-----------+------------------------------------+----------------+
| K_B | Burst frame size during the | 13.5 KB* |
| | transient period | |
+-----------+------------------------------------+----------------+
| t0 | Reference frame interval 1/FPS | 33 ms |
+-----------+------------------------------------+----------------+
| B0 | Reference frame size R_v/8/FPS | 4.17 KB |
+-----------+------------------------------------+----------------+
| | Scaling parameter of the zero-mean | |
| | Laplacian distribution describing | |
| SCALE_t | deviations in normalized frame | 0.15 |
| | interval (t-t0)/t0 | |
+-----------+------------------------------------+----------------+
| | Scaling parameter of the zero-mean | |
| | Laplacian distribution describing | |
| SCALE_B | deviations in normalized frame | 0.15 |
| | size (B-B0)/B0 | |
+-----------+------------------------------------+----------------+
| R_min | Minimum rate supported by video | 150 kbps |
| | encoder type or content activity | |
+-----------+------------------------------------+----------------+
| R_max | Maximum rate supported by video | 1.5 Mbps |
| | encoder type or content activity | |
+===========+====================================+================+
* Example value of K_B for a video stream encoded at 720p and
30 frames per second using H.264/AVC encoder
Figure 2: List of Tunable Parameters in a Statistical Video Traffic
Source Model
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5.1. Time-Damped Response to Target-Rate Update
While the congestion control module can update its target bitrate
request R_v at any time, the statistical model dictates that the
encoder will only react to such changes tau_v seconds after 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 and Oscillation during the Transient Period
The output bitrate R_o during the period [t, t+tau_v] is considered
to be in a transient state when reacting to abrupt changes in target
rate. Based on observations from video encoder output, the encoder
reaction to a new target bitrate request can be characterized by high
variations in output frame sizes. It is assumed in the model that
the overall average output bitrate R_o during this transient period
matches the target bitrate R_v. Consequently, the occasional burst
of large frames is followed by smaller-than-average encoded frames.
This temporary burst is characterized by two parameters:
o burst duration K_d: Number of frames in the burst event, and
o burst frame size K_B: Size of the initial burst frame, which is
typically significantly larger than the average frame size at
steady state.
It can be noted that these burst parameters can also be used to mimic
the insertion of a large on-demand I-frame in the presence of severe
packet losses. The values of K_d and K_B typically depend on the
type of video codec, spatial and temporal resolution of the encoded
stream, as well as the activity level in the video content.
5.3. Output-Rate Fluctuation at Steady State
The output bitrate R_o during steady state is modeled as randomly
fluctuating around the target bitrate R_v. The output traffic can be
characterized as the combination of two random processes that denote
the frame interval t and output frame size B over time, which are the
two major sources of variations in the encoder output. For
simplicity, the deviations of t and B from their respective reference
levels are modeled as independent and identically distributed (i.i.d)
random variables following the Laplacian distribution [Papoulis].
More specifically:
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o Fluctuations in frame interval: The intervals between adjacent
frames have been observed to fluctuate around the reference
interval of t0 = 1/FPS. Deviations in normalized frame interval
DELTA_t = (t-t0)/t0 can be modeled by a zero-mean Laplacian
distribution with scaling parameter SCALE_t. The value of SCALE_t
dictates the "width" of the Laplacian distribution and therefore
the amount of fluctuation in actual frame intervals (t) with
respect to the reference frame interval t0.
o Fluctuations in frame size: The output-encoded frame sizes also
tend to fluctuate around the reference frame size B0=R_v/8/FPS.
Likewise, deviations in the normalized frame size DELTA_B =
(B-B0)/B0 can be modeled by a zero-mean Laplacian distribution
with scaling parameter SCALE_B. The value of SCALE_B dictates the
"width" of this second Laplacian distribution and correspondingly
the amount of fluctuations in output frame sizes (B) with respect
to the reference target B0.
Both values of SCALE_t and SCALE_B can be obtained via parameter
fitting from empirical data captured for a given video encoder.
Example values are listed in Figure 2 based on empirical data
presented in [IETF-Interim].
5.4. Rate Range Limit Imposed by Video Content
The output bitrate 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 the proposed
statistical model, these parameters are specified by the application.
6. A Trace-Driven Model
The second approach for modeling a video traffic source is trace-
driven. This can be achieved by running an actual live video encoder
on a set of chosen raw video sequences and using the encoder's output
traces for constructing a synthetic video source. With this
approach, the recorded video traces naturally exhibit temporal
fluctuations around a given target bitrate request R_v from the
congestion control module.
The following list summarizes the main steps of this approach:
1. Choose one or more representative raw video sequences.
2. Encode the sequence(s) using an actual live video encoder.
Repeat the process for a number of bitrates. Keep only the
sequence of frame sizes for each bitrate.
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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 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 video traffic source contains
"encoded" frames with dummy contents but with realistic sizes.
Section 6.1 explains the first three steps (1-3), and Section 6.2
elaborates on the remaining two steps (4-5). Finally, Section 6.3
briefly discusses the possibility to extend the trace-driven model
for supporting time-varying frame rate and/or time-varying frame
resolution.
6.1. Choosing the Video Sequence and Generating the Traces
The first step is a careful choice of a set of video sequences that
are representative of the target use cases for the video traffic
model. For the example use case of interactive video conferencing,
it is recommended to choose a sequence with content that resembles a
"talking head", e.g., from a news broadcast or recording of an actual
video conferencing 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. If it is too short, there will be an obvious periodic
pattern in the output frame sizes, leading to biased results when
evaluating congestion control performance. It has been empirically
determined that a sequence 2 to 4 minutes in length sufficiently
avoids the periodic pattern.
Given the chosen raw video sequence, denoted "S", one can use a live
encoder, e.g., some implementation of [H264] or [H265], to produce a
set of encoded sequences. As discussed in Section 3, the output
bitrate of the live encoder can be achieved by tuning three input
parameters: quantization step size, frame rate, and picture
resolution. In order to simplify the choice of these parameters for
a given target rate, one can typically assume a fixed frame rate
(e.g., 30 fps) and a fixed resolution (e.g., 720p) when configuring
the live encoder. See Section 6.3 for a discussion on how to relax
these assumptions.
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Following these simplifications, the chosen encoder can be configured
to start at a constant target bitrate, then vary the quantization
step size (internally via the video encoder rate controller) to meet
various externally specified target rates. It can be further assumed
the first frame is encoded as an I-frame and the rest are P-frames
(see, e.g., [H264] for definitions of I-frames and P-frames). For
live encoding, the encoder rate-control algorithm typically does not
use knowledge of frames in the future when encoding a given frame.
Given the minimum and maximum bitrates at which the synthetic codec
is to operate (denoted as "R_min" and "R_max", see Section 4), the
entire range of target bitrates can be divided into n_s steps. This
leads to an encoding bitrate ladder of (n_s + 1) choices equally
spaced apart by the step length l = (R_max - R_min)/n_s. The
following simple algorithm is used to encode the raw video sequence.
r = R_min
while r <= R_max do
Traces[r] = encode_sequence(S, r, e)
r = r + l
The function encode_sequence takes as input parameters, respectively,
a raw video sequence (S), a constant target rate (r), and an encoder
rate-control algorithm (e); 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 whose
values are vectors of frame sizes.
The choice of a value for the number of bitrate steps n_s is
important, since it determines the number of vectors of frame sizes
stored in the map Traces. The minimum value one can choose for n_s
is 1; the maximum value depends on the amount of memory available for
holding the map Traces. A reasonable value for n_s is one that
results in steps of length l = 200 kbps. Section 6.2.2 will discuss
further the choice of step length l.
Finally, note that, as mentioned in previous sections, R_min and
R_max may be modified after the initial sequences are encoded.
Henceforth, for notational clarity, we refer to the bitrate range of
the trace file as [Rf_min, Rf_max]. The algorithm described in
Section 6.2.1 also covers the cases when the current target bitrate
is less than Rf_min or greater than Rf_max.
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6.2. Using the Traces in the Synthetic Codec
The main idea behind the trace-driven synthetic codec is that it
mimics the rate-adaptation behavior of a real live codec upon dynamic
updates of the target bitrate request R_v by the congestion control
module. It does so by switching to a different frame-size vector
stored in the map Traces when needed.
6.2.1. Main Algorithm
The main algorithm for rate adaptation in the synthetic codec
maintains two variables: r_current and t_current.
o The variable r_current points to one of the keys of map Traces.
Upon a change in the value of R_v, typically because the
congestion controller detects that the network conditions have
changed, r_current is updated based on R_v as follows:
R_ref = min (Rf_max, max(Rf_min, R_v))
r_current = r
such that
(r in keys(Traces) and
r <= R_ref and
(not(exists) r' in keys(Traces) such that r <r'<= R_ref))
o The variable 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. It is assumed that all vectors stored in Traces have the
same size, denoted as "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 large I-frame followed by several smaller-
than-average P-frames. A typical value of SkipFrames is 20, although
it could be set to 0 if one is interested in studying the effect of
sending I-frames periodically.
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The initial value of r_current is set to R_min, and the initial value
of t_current is set to 0.
When a new frame is due, its size can be calculated following one of
the three cases below:
a) Rf_min <= R_v < Rf_max: The output frame size is calculated via
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 - r_current) / l
framesize = size_hi*distance_lo + size_lo*(1-distance_lo)
b) R_v < Rf_min: The output frame size is calculated via scaling
with respect to the lowest bitrate Rf_min in the trace file, as
follows:
w = R_v / Rf_min
framesize = max(fs_min, factor * Traces[Rf_min][t_current])
c) R_v >= Rf_max: The output frame size is calculated by scaling
with respect to the highest bitrate Rf_max in the trace file, as
follows:
w = R_v / Rf_max
framesize = min(fs_max, w * Traces[Rf_max][t_current])
In cases b) and c), floating-point arithmetic is used for computing
the scaling factor "w". The resulting value of the instantaneous
frame size (framesize) is further clipped within a reasonable range
between fs_min (e.g., 10 bytes) and fs_max (e.g., 1 MB).
6.2.2. Notes to the Main Algorithm
Note that the main algorithm as described above can be further
extended to mimic some additional typical behaviors of a live video
encoder. Two examples are given below:
o I-frames on demand: The synthetic codec can 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 incoming
interface (see (a) in Figure 1) is augmented with a new function
to request a new I-frame. Upon calling such function, t_current
is reset to 0.
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o Variable step length l between R_min and R_max: In the main
algorithm, the step length l is fixed for ease of explanation.
However, if the range [R_min, R_max] is very wide, it is also
possible to define a set of intermediate encoding rates with
variable step length. The rationale behind this modification is
that the difference between 400 and 600 kbps as target bitrate is
much more significant than the difference between 4400 kbps and
4600 kbps. For example, one could define steps of length 200 kbps
under 1 Mbps, then steps of length 300 kbps between 1 Mbps and 2
Mbps, then 400 kbps between 2 Mbps and 3 Mbps, and so on.
6.3. Varying Frame Rate and Resolution
The trace-driven synthetic codec model explained in this section is
relatively simple due to the choice of fixed frame rate and frame
resolution. The model can be extended further to accommodate
variable frame rate and/or variable spatial resolution.
When the encoded picture quality at a given bitrate is low, one can
potentially decrease either the frame rate (if the video sequence is
currently in low motion) or the spatial resolution in order to
improve quality of experience (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-quantization step size, frame rate, and
spatial resolution in order to maximize the quality of live video
codecs [Ozer2011] [Hu2012]. Future work may consider extending the
trace-driven 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 by
the media transport module. Investigation of varying frame rate and
resolution are left for future work.
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7. Combining the Two Models
It is worthwhile noting that the statistical and trace-driven models
each have their own advantages and drawbacks. Both models are fairly
simple to implement. It takes significantly greater effort to fit
the parameters of a statistical model to actual encoder output data.
In contrast, it is straightforward for a trace-driven model to obtain
encoded frame-size data. Once validated, the statistical model is
more flexible in mimicking a wide range of encoder/content behaviors
by simply varying the corresponding parameters in the model. In this
regard, a trace-driven model relies, by definition, on additional
data-collection efforts for accommodating new codecs or video
contents.
In general, the trace-driven model is more realistic for mimicking
the ongoing steady-state behavior of a video traffic source with
fluctuations around a constant target rate. In contrast, the
statistical model is more versatile for simulating the behavior of a
video stream in transient, such as when encountering sudden rate
changes. It is also possible to combine both methods into a hybrid
model. In this case, the steady-state behavior is driven by traces
during steady state and the transient-state behavior is driven by the
statistical model.
transient +---------------+
state | Generate next |
+------>| K_d transient |
+-----------------+ / | frames |
R_v | Compare against | / +---------------+
------>| previous |/
| target bitrate |\
+-----------------+ \ +---------------+
\ | Generate next |
+------>| frame from |
steady | trace |
state +---------------+
Figure 3: A Hybrid Video Traffic Model
As shown in Figure 3, the video traffic model operates in a transient
state if the requested target rate R_v is substantially different
from the previous target; otherwise, it operates in a steady state.
During the transient state, a total of K_d frames are generated by
the statistical model, resulting in one (1) big burst frame with size
K_B followed by K_d-1 smaller frames. When operating at steady
state, the video traffic model simply generates a frame according to
the trace-driven model given the target rate while modulating the
frame interval according to the distribution specified by the
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statistical model. One example criterion for determining whether the
traffic model should operate in a transient state is whether the rate
change exceeds 10% of the previous target rate. Finally, as this
model follows transient-state behavior dictated by the statistical
model, upon a substantial rate change, the model will follow the
time-damping mechanism as defined in Section 5.1, which is governed
by parameter tau_v.
8. Reference Implementation
The statistical, trace-driven, and hybrid models as described in this
document have been implemented as a stand-alone, platform-independent
synthetic traffic source module. It can be easily integrated into
network simulation platforms such as [ns-2] and [ns-3], as well as
testbeds using a real network. The stand-alone traffic source module
is available as an open-source implementation at [Syncodecs].
9. IANA Considerations
This document has no IANA actions.
10. Security Considerations
The synthetic video traffic models as described in this document do
not impose any security threats. They are designed to mimic
realistic traffic patterns for evaluating candidate RTP-based
congestion control algorithms so as to ensure stable operations of
the network. It is RECOMMENDED that candidate algorithms be tested
using the video traffic models presented in this document before wide
deployment over the Internet. If the generated synthetic traffic
flows are sent over the Internet, they also need to be congestion
controlled.
11. References
11.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
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RFC 8593 Video Traffic Models for RTP May 2019
11.2. Informative References
[H264] ITU-T, "Advanced video coding for generic audiovisual
services", Recommendation H.264, April 2017,
<https://www.itu.int/rec/T-REC-H.264>.
[H265] ITU-T, "High efficiency video coding",
Recommendation H.265, February 2018,
<https://www.itu.int/rec/T-REC-H.265>.
[Hu2012] Hu, H., Ma, Z., and Y. Wang, "Optimization of Spatial,
Temporal and Amplitude Resolution for Rate-Constrained
Video Coding and Scalable Video Adaptation", Proc. 19th
IEEE International Conference on Image Processing (ICIP),
DOI 10.1109/ICIP.2012.6466960, September 2012.
[IETF-Interim]
Zhu, X., Mena, S., and Z. Sarker, "Update on RMCAT Video
Traffic Model: Trace Analysis and Model Update", IETF
RMCAT Virtual Interim, April 2017,
<https://www.ietf.org/proceedings/interim-2017-rmcat-
01/slides/slides-interim-2017-rmcat-01-sessa-update-on-
video-traffic-model-draft-00.pdf>.
[ns-2] "The Network Simulator - ns-2", December 2015,
<https://nsnam.sourceforge.net/wiki/index.php/
User_Information>.
[ns-3] "NS-3 Network Simulator", <https://www.nsnam.org/>.
[Ozer2011] Ozer, J., "Video Compression for Flash, Apple Devices and
HTML5", Galax: Doceo Publishing, ISBN-13: 978-0976259503,
2011.
[Papoulis] Papoulis, A. and S. Pillai, "Probability, Random Variables
and Stochastic Processes", London: McGraw-Hill Europe,
ISBN-13: 978-0071226615, 2002.
[RFC5104] Wenger, S., Chandra, U., Westerlund, M., and B. Burman,
"Codec Control Messages in the RTP Audio-Visual Profile
with Feedback (AVPF)", RFC 5104, DOI 10.17487/RFC5104,
February 2008, <https://www.rfc-editor.org/info/rfc5104>.
Zhu, et al. Informational [Page 18]
RFC 8593 Video Traffic Models for RTP May 2019
[Syncodecs]
"Syncodecs: Synthetic codecs for evaluation of RMCAT
work", commit a92d6c8, May 2018,
<https://github.com/cisco/syncodecs>.
[Tanwir2013]
Tanwir, S. and H. Perros, "A Survey of VBR Video Traffic
Models", IEEE Communications Surveys and Tutorials, Volume
15, Issue 4, p. 1778-1802,
DOI 10.1109/SURV.2013.010413.00071, January 2013.
Authors' Addresses
Xiaoqing Zhu
Cisco Systems
12515 Research Blvd., Building 4
Austin, TX 78759
United States of America
Email: xiaoqzhu@cisco.com
Sergio Mena
Cisco Systems
EPFL, Quartier de l'Innovation, Batiment E
Ecublens, Vaud 1015
Switzerland
Email: semena@cisco.com
Zaheduzzaman Sarker
Ericsson AB
Torshamnsgatan 23
Stockholm, SE 164 83
Sweden
Phone: +46 10 717 37 43
Email: zaheduzzaman.sarker@ericsson.com
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