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rfc:rfc8593

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.

Zhu, et al. Informational [Page 1] RFC 8593 Video Traffic Models for RTP May 2019

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
 (https://trustee.ietf.org/license-info) in effect on the date of
 publication of this document.  Please review these documents
 carefully, as they describe your rights and restrictions with respect
 to this document.  Code Components extracted from this document must
 include Simplified BSD License text as described in Section 4.e of
 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

Zhu, et al. Informational [Page 2] RFC 8593 Video Traffic Models for RTP May 2019

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.

Zhu, et al. Informational [Page 3] RFC 8593 Video Traffic Models for RTP May 2019

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.

Zhu, et al. Informational [Page 4] RFC 8593 Video Traffic Models for RTP May 2019

 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).

Zhu, et al. Informational [Page 5] RFC 8593 Video Traffic Models for RTP May 2019

 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.

Zhu, et al. Informational [Page 6] RFC 8593 Video Traffic Models for RTP May 2019

                          +-------------+
                          |             |  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].

Zhu, et al. Informational [Page 7] RFC 8593 Video Traffic Models for RTP May 2019

   +===========+====================================+================+
   | 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

Zhu, et al. Informational [Page 8] RFC 8593 Video Traffic Models for RTP May 2019

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:

Zhu, et al. Informational [Page 9] RFC 8593 Video Traffic Models for RTP May 2019

 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.

Zhu, et al. Informational [Page 10] RFC 8593 Video Traffic Models for RTP May 2019

 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.

Zhu, et al. Informational [Page 11] RFC 8593 Video Traffic Models for RTP May 2019

 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.

Zhu, et al. Informational [Page 12] RFC 8593 Video Traffic Models for RTP May 2019

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.

Zhu, et al. Informational [Page 13] RFC 8593 Video Traffic Models for RTP May 2019

 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.

Zhu, et al. Informational [Page 14] RFC 8593 Video Traffic Models for RTP May 2019

 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.

Zhu, et al. Informational [Page 15] RFC 8593 Video Traffic Models for RTP May 2019

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

Zhu, et al. Informational [Page 16] RFC 8593 Video Traffic Models for RTP May 2019

 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>.

Zhu, et al. Informational [Page 17] 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

Zhu, et al. Informational [Page 19]

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