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
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
用于RTP拥塞控制评估的视频流量模型
Abstract
摘要
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.
本文描述了两个用于评估RTP拥塞控制算法的参考视频流量模型。第一个模型统计描述了实时视频编码器响应目标视频速率上不断变化的请求的行为。第二个模型是跟踪驱动的，模拟来自高分辨率测试序列的实际编码视频帧大小的输出。这两种模型的设计都是为了在建模实时视频流量源和拥塞控制模块之间的交互时在简单性、可重复性和真实性之间取得平衡。最后，本文描述了如何将这两种方法组合成一个混合模型。
Status of This Memo
关于下段备忘
This document is not an Internet Standards Track specification; it is published for informational purposes.
本文件不是互联网标准跟踪规范；它是为了提供信息而发布的。
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.
本文件是互联网工程任务组（IETF）的产品。它代表了IETF社区的共识。它已经接受了公众审查，并已被互联网工程指导小组（IESG）批准出版。并非IESG批准的所有文件都适用于任何级别的互联网标准；见RFC 7841第2节。
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.
有关本文件当前状态、任何勘误表以及如何提供反馈的信息，请访问https://www.rfc-editor.org/info/rfc8593.
Copyright Notice
版权公告
Copyright (c) 2019 IETF Trust and the persons identified as the document authors. All rights reserved.
版权（c）2019 IETF信托基金和被确定为文件作者的人员。版权所有。
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.
本文件受BCP 78和IETF信托有关IETF文件的法律规定的约束(https://trustee.ietf.org/license-info)自本文件出版之日起生效。请仔细阅读这些文件，因为它们描述了您对本文件的权利和限制。从本文件中提取的代码组件必须包括信托法律条款第4.e节中所述的简化BSD许可证文本，并提供简化BSD许可证中所述的无担保。
Table of Contents
目录
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
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
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.
在评估为实时交互媒体设计的候选拥塞控制算法时，重要的是考虑由实时视频编码器生成的流量模式的特征。与能够完全符合来自拥塞控制模块的速率变化请求的合成流量源不同，实时视频编码器在对此类变化作出反应时可能反应迟缓。由于编码器速率控制过程中的不确定性，实时视频编码器的输出速率通常也偏离目标速率。因此，实时媒体流的端到端延迟和损失性能可进一步受到实时编码器引入的速率变化的影响。
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.
另一方面，候选RTP拥塞控制算法的评估结果应主要反映拥塞控制模块的性能，并在一定程度上与任何特定视频编解码器的特性解耦。评估测试是可重复的，并且容易在不同的候选算法之间复制，这也是可取的。
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.
在上述考虑因素之间取得平衡的一种方法是使用捕获实时视频编码器行为关键特征的合成视频流量源模型来评估拥塞控制算法。合成流量模型还应包含可调参数，以便能够灵活调整以反映真实世界实时视频编码器行为的广泛变化。为此，本文件介绍了两种参考模型。第一种是基于统计建模。第二个是由帧大小和从真实编码器记录的间隔跟踪驱动的。本文档还讨论了每种方法的优缺点，以及如何将这两种方法组合成一个混合模型。
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.
本文件中的关键词“必须”、“不得”、“必需”、“应”、“不应”、“建议”、“不建议”、“可”和“可选”在所有大写字母出现时（如图所示）应按照BCP 14[RFC2119][RFC8174]所述进行解释。
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.
实时视频编码器通过基于其对视频内容的估计（例如，运动和场景复杂度）改变其编码参数（例如量化步长、帧速率和图片分辨率），采用编码器速率控制以满足目标速率。然而，在实践中，有几个因素阻止输出视频速率与输入目标速率完全一致。
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.
由于捕获的视频场景中的不确定性，输出速率通常偏离指定目标。在目标速率发生显著变化的情况下，编码器的输出帧大小有时会在输出速率收敛到新目标之前短暂波动一段时间。最后，虽然实时会话中的大多数帧以预测模式编码（即，[H264]中的P帧），但编码器偶尔可以生成大的帧内编码帧（即，[H264]中定义的i帧）或部分包含帧内编码块的帧，以尝试从丢失中恢复，从而与接收机重新同步，或者在响应目标速率或空间分辨率变化的瞬态期间。
Hence, a synthetic video source should have the following capabilities:
因此，合成视频源应具有以下功能：
o To change bitrate. This includes the ability to change frame rate and/or spatial resolution or to skip frames upon request.
o 改变比特率。这包括更改帧速率和/或空间分辨率或根据请求跳过帧的能力。
o To fluctuate around the target bitrate specified by the congestion control module.
o 围绕拥塞控制模块指定的目标比特率波动。
o To show a delay in convergence to the target bitrate.
o 显示收敛到目标比特率的延迟。
o To generate intra-coded or repair frames on demand.
o 按需生成帧内编码或修复帧。
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.
虽然在开发合成视频流量模型时存在许多不同的方法，但希望结果遵循以下几个共同特征。
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 低计算复杂度：该模型应在计算上是轻量级的，否则，它就不能完全替代实时视频编码器。
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 时间模式相似性：由模型生成的单个流量跟踪实例应模仿来自真实视频编码器的时间模式。
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 统计相似性：合成流量源应在统计特征方面与真实视频编码器的结果相匹配，例如比特率的平均值、方差、峰值和自相关系数。同样重要的是，统计相似性应在从数十毫秒到亚秒的不同时间尺度上保持。
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).
o 广泛的覆盖范围：该模型应易于配置，以覆盖广泛的编解码器行为（例如，实时编码器速率控制中的快速或慢速反应时间）和视频内容变化（例如，从高到低的运动）。
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.
这些不同的行为特征可以通过简单的统计建模或跟踪驱动的方法来描述。第5节和第6节分别提供了每种方法的示例。第7节讨论了如何将这两种模型组合在一起。
4. Interactions between Synthetic Video Traffic Source and Other Components at the Sender
4. 合成视频流量源与发送方其他组件之间的交互
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.
图1描述了合成视频业务源与发送方的其他组件（如应用程序、拥塞控制模块、媒体分组传输模块等）的交互。如第5节和第6节稍后所述，这两个参考模型遵循相同的交互集。
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.
合成视频源动态生成具有不同大小和间隔的虚拟视频帧序列。这些虚拟帧由其他模块处理，以便通过网络传输视频流。在视频传输会话的生存期内，合成视频源通常需要调整其编码比特率，有时还需要调整空间分辨率和帧速率。
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.
在此模型中，合成视频源模块具有一组传入和传出接口调用，允许与其他模块交互。下面是一些可能的传入接口调用，在图1中标记为（a），合成视频流量源可以接受这些调用。该列表并非详尽无遗，如有必要，可通过其他接口调用进行补充。
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).
o 目标比特率R_v：以每秒比特数（bps）度量的目标比特率请求。通常，拥塞控制模块计算目标比特率并随时间动态更新。根据使用的拥塞控制算法，更新请求可以是周期性的（例如，每秒一次），也可以是按需的（例如，仅当观察到网络上的带宽急剧变化时）。
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 目标帧速率FPS：在给定时间以每秒帧数为单位测量的瞬时帧速率。这取决于本机相机捕获帧速率以及应用程序或用户配置的目标/首选帧速率。
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 目标帧分辨率XY：以像素为单位指示首选帧分辨率的二维向量。随着时间的推移，有几个因素控制合成视频源所需的分辨率。此类因素的示例包括本机相机的捕获分辨率和目标屏幕的显示大小。目标帧分辨率还取决于当前目标比特率R_v，因为将非常低的空间分辨率与非常高的比特率配对是没有意义的，反之亦然。
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 即时跳帧：例如，当检测到可用网络带宽急剧减少时，请求跳过一个或多个捕获视频帧的编码。
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].
o 按需生成帧内（I）帧：当观察到严重的数据包丢失时，请求对另一个I帧进行编码以避免在接收器处进一步的错误传播。此请求通常来自错误控制模块。它可以由发送方或接收方通过[RFC5104]中定义的完整内部请求（FIR）消息发起。
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.
图1中标记为（b）的传出接口调用示例是速率范围[R_min，R_max]。这里，R_min和R_max用于捕获给定输入视频内容的实际实时视频编码器能够生成的动态速率范围。这通常取决于视频内容复杂度和/或显示类型（例如，对于运动复杂度较高的视频内容或分辨率较高的显示，R_max较高）。因此，这些值不会随R_v的变化而变化，但如果内容发生变化，这些值可能会随时间而变化。
+-------------+
| | dummy encoded
| Synthetic | video frames
| Video | -------------->
| Source |
| |
+--------+----+
/|\ |
| |
-------------------+ +-------------------->
interface from interface to
other modules (a) other modules (b)
+-------------+
| | 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
图1：合成视频编码器与发送方其他模块之间的交互
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].
本节介绍一个简单的实时视频流量源统计模型。图2总结了此统计模型中的可调参数列表。关于视频流量源行为建模常用方法的更全面调查，请参见[Tanwir2013]。
+===========+====================================+================+
| 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 | |
+===========+====================================+================+
+===========+====================================+================+
| 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
* 使用H.264/AVC编码器以720p和每秒30帧编码的视频流的K_B的示例值
Figure 2: List of Tunable Parameters in a Statistical Video Traffic Source Model
图2：统计视频流量源模型中的可调参数列表
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.
尽管拥塞控制模块可随时更新其目标比特率请求R_v，但统计模型规定编码器仅在前一速率转换后的tau_v秒对此类变化作出反应。换句话说，当编码器在时间t对速率改变请求做出响应时，它将忽略所有后续速率改变请求，直到时间t+tau_v。
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.
当对目标速率的突然变化作出反应时，周期[t，t+tau_v]期间的输出比特率R_o被认为处于瞬态。基于对视频编码器输出的观察，编码器对新目标比特率请求的反应可以通过输出帧大小的高度变化来表征。在该模型中，假设该瞬态期间的总平均输出比特率R_o与目标比特率R_v匹配。因此，偶尔的大帧突发之后是小于平均编码帧的突发。
This temporary burst is characterized by two parameters:
此临时突发具有两个参数：
o burst duration K_d: Number of frames in the burst event, and
o 突发持续时间K_d：突发事件中的帧数，以及
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.
o 突发帧大小K_B：初始突发帧的大小，通常显著大于稳态下的平均帧大小。
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.
可以注意到，这些突发参数还可用于在存在严重分组丢失的情况下模拟大的按需I帧的插入。K_d和K_B的值通常取决于视频编解码器的类型、编码流的空间和时间分辨率以及视频内容中的活动级别。
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:
稳态期间的输出比特率R_o被建模为围绕目标比特率R_v随机波动。输出业务可以被描述为表示帧间隔t和输出帧大小B随时间变化的两个随机过程的组合，这是编码器输出中变化的两个主要来源。为简单起见，t和B与各自参考水平的偏差被建模为拉普拉斯分布[Papoulis]下的独立同分布（i.i.d）随机变量。更具体地说：
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 帧间隔波动：已观察到相邻帧之间的间隔在t0=1/FPS的参考间隔周围波动。归一化帧间隔DELTA_t=（t-t0）/t0中的偏差可由具有标度参数SCALE_t的零平均拉普拉斯分布建模。SCALE_t的值指示拉普拉斯分布的“宽度”，因此实际帧间隔（t）相对于参考帧间隔t0的波动量。
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.
o 帧大小波动：输出编码帧大小也倾向于在参考帧大小B0=R_v/8/FPS周围波动。类似地，归一化帧大小DELTA_B=（B-B0）/B0中的偏差可由具有缩放参数SCALE_B的零平均拉普拉斯分布建模。SCALE_B的值指示该第二拉普拉斯分布的“宽度”，并相应地指示相对于参考目标B0的输出帧大小（B）的波动量。
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].
SCALE_t和SCALE_B的值都可以通过参数拟合从给定视频编码器捕获的经验数据获得。基于[IETF-Middial]中给出的经验数据，图2中列出了示例值。
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.
输出比特率R_o在动态范围[R_min，R_max]内进一步剪裁，该动态范围实际上由捕获的视频内容的场景和运动复杂性决定。在建议的统计模型中，这些参数由应用程序指定。
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.
第二种建模视频流量源的方法是跟踪驱动。这可以通过在一组选定的原始视频序列上运行实际的实时视频编码器并使用编码器的输出轨迹来构建合成视频源来实现。通过这种方法，记录的视频跟踪自然地围绕来自拥塞控制模块的给定目标比特率请求R_v呈现时间波动。
The following list summarizes the main steps of this approach:
以下列表总结了此方法的主要步骤：
1. Choose one or more representative raw video sequences.
1. 选择一个或多个具有代表性的原始视频序列。
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.
2. 使用实际的实时视频编码器对序列进行编码。对多个比特率重复该过程。仅保留每个比特率的帧大小序列。
3. Construct a data structure that contains the output of the previous step. The data structure should allow for easy bitrate lookup.
3. 构造包含上一步输出的数据结构。数据结构应允许轻松查找比特率。
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.
4. 当控制器发出目标比特率请求R_v时，在先前存储的比特率中查找最近的比特率。使用为这些比特率存储的帧大小序列来近似输出的帧大小。
5. The output of the synthetic video traffic source contains "encoded" frames with dummy contents but with realistic sizes.
5. 合成视频流量源的输出包含具有虚拟内容但具有真实大小的“编码”帧。
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.
第6.1节解释了前三个步骤（1-3），第6.2节阐述了其余两个步骤（4-5）。最后，第6.3节简要讨论了扩展跟踪驱动模型以支持时变帧速率和/或时变帧分辨率的可能性。
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.
第一步是仔细选择一组视频序列，这些视频序列代表视频流量模型的目标用例。对于交互式视频会议的示例用例，建议选择内容类似于“谈话头”的序列，例如，从新闻广播或实际视频会议呼叫的录制中。
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.
所选视频序列的长度是一个折衷方案。如果太长，则很难管理包含跟踪的数据结构。如果时间太短，则输出帧大小会出现明显的周期性模式，从而导致在评估拥塞控制性能时出现偏差。根据经验确定，长度为2到4分钟的序列足以避免周期模式。
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.
给定所选择的原始视频序列，表示为“S”，可以使用实时编码器，例如[H264]或[H265]的一些实现来产生一组编码序列。如第3节所述，实时编码器的输出比特率可以通过调整三个输入参数来实现：量化步长、帧速率和图片分辨率。为了简化针对给定目标速率的这些参数的选择，在配置实时编码器时，通常可以假设固定帧速率（例如，30fps）和固定分辨率（例如，720p）。有关如何放松这些假设的讨论，请参见第6.3节。
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.
在这些简化之后，所选择的编码器可以配置为以恒定的目标比特率开始，然后改变量化步长（内部通过视频编码器速率控制器）以满足各种外部指定的目标速率。可以进一步假设第一帧被编码为I帧，其余为P帧（参见，例如，关于I帧和P帧的定义，参见[H264]）。对于实时编码，编码器速率控制算法在编码给定帧时通常不使用未来帧的知识。
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.
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
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.
函数encode_序列分别以原始视频序列（S）、恒定目标速率（r）和编码器速率控制算法（e）作为输入参数；它返回一个向量，其中包含按编码顺序排列的帧大小。输出向量存储在称为“跟踪”的映射结构中，其键为比特率，其值为帧大小的向量。
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.
比特率步数n_s的值的选择很重要，因为它决定了存储在映射跟踪中的帧大小向量的数量。可以为n_s选择的最小值为1；最大值取决于可用于保存贴图轨迹的内存量。n_s的合理值是导致长度l=200 kbps的步长的值。第6.2.2节将进一步讨论步长l的选择。
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.
最后，请注意，如前几节所述，在对初始序列进行编码之后，可以修改R_min和R_max。此后，为了清楚起见，我们将跟踪文件的比特率范围称为[Rf_min，Rf_max]。第6.2.1节中描述的算法还包括当前目标比特率小于Rf_min或大于Rf_max的情况。
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.
跟踪驱动的合成编解码器背后的主要思想是，它模仿真实实时编解码器在拥塞控制模块动态更新目标比特率请求R_v时的速率自适应行为。它通过在需要时切换到存储在贴图轨迹中的不同帧大小向量来实现。
The main algorithm for rate adaptation in the synthetic codec maintains two variables: r_current and t_current.
合成编解码器中用于速率自适应的主要算法维护两个变量：r_current和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:
o 变量r_current指向映射跟踪的一个键。当R_v的值发生变化时，通常是因为拥塞控制器检测到网络状况发生了变化，R_current根据R_v更新如下：
R_ref = min (Rf_max, max(Rf_min, R_v))
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))
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:
o 变量t_current是存储在记录道[r_current]中的帧大小向量的索引。每次新帧到期时，它都会更新。假设存储在记录道中的所有向量具有相同的大小，表示为“大小\记录道”。以下方程式控制t_电流的更新：
if t_current < SkipFrames then
t_current = t_current + 1
else
t_current = ((t_current + 1 - SkipFrames)
% (size_traces-SkipFrames)) + SkipFrames
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.
其中，运算符“%”表示模，SkipFrames是一个预定义的常数，表示在t_current结束后，在帧大小向量的开始处要跳过的帧数。恒定SkipFrames的要点是避免周期性地发送一个较大的I帧和几个小于平均P帧的效果。SkipFrames的典型值为20，但如果有人有兴趣研究周期性发送I帧的效果，则可以将其设置为0。
The initial value of r_current is set to R_min, and the initial value of t_current is set to 0.
r_电流的初始值设置为r_min，t_电流的初始值设置为0。
When a new frame is due, its size can be calculated following one of the three cases below:
当新框架到期时，其尺寸可根据以下三种情况之一计算：
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:
a) Rf_min<=R_v<Rf_max：输出帧大小通过记录道[R_current]和记录道[R_current+l]中出现的帧大小的线性插值来计算。插值操作如下所示：
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)
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:
b) R_v<Rf_min：输出帧大小是根据跟踪文件中的最低比特率Rf_min进行缩放计算的，如下所示：
w = R_v / Rf_min
framesize = max(fs_min, factor * Traces[Rf_min][t_current])
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:
c) R_v>=Rf_max：输出帧大小是根据跟踪文件中的最高比特率Rf_max进行缩放计算的，如下所示：
w = R_v / Rf_max
framesize = min(fs_max, w * Traces[Rf_max][t_current])
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).
在情况b）和c）中，浮点运算用于计算比例因子“w”。瞬时帧大小（framesize）的结果值进一步剪裁在fs_min（例如，10字节）和fs_max（例如，1 MB）之间的合理范围内。
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:
注意，如上所述的主要算法可以进一步扩展，以模拟实时视频编码器的一些额外的典型行为。以下是两个例子：
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.
o I-frames on demand（按需I-frames on demand）：可以扩展合成编解码器，以模拟I-frames on demand（按需I-frames发送），例如，作为对丢失的反应。为了实现这个扩展，编解码器的传入接口（参见图1中的（a））增加了一个新函数，以请求一个新的I帧。调用此函数时，t_电流重置为0。
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.
o R_min和R_max之间的可变步长l：在主算法中，步长l是固定的，以便于解释。然而，如果范围[R_min，R_max]非常宽，也可以定义一组具有可变步长的中间编码速率。这种修改背后的基本原理是，作为目标比特率的400和600 kbps之间的差异比4400 kbps和4600 kbps之间的差异更为显著。例如，可以在1 Mbps以下定义长度为200 kbps的步长，然后在1 Mbps和2 Mbps之间定义长度为300 kbps的步长，然后在2 Mbps和3 Mbps之间定义长度为400 kbps的步长，依此类推。
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.
由于选择了固定的帧速率和帧分辨率，本节介绍的跟踪驱动合成编解码器模型相对简单。该模型可以进一步扩展以适应可变帧速率和/或可变空间分辨率。
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).
当给定比特率下的编码图片质量较低时，可以潜在地降低帧速率（如果视频序列当前处于低运动状态）或空间分辨率，以提高整体编码视频中的体验质量（QoE）。另一方面，如果目标比特率增加到单个帧的画面质量不再有可感知的改善的点，则可以增加空间分辨率或帧率（如果视频当前处于高运动状态，则有用）。
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.
为了最大限度地提高实时视频编解码器的质量，已经提出了许多技术来随时间选择编码器量化步长、帧速率和空间分辨率的最佳组合[Ozer2011][Hu2012]。未来的工作可以考虑扩展跟踪驱动编解码器以适应可变帧速率和/或分辨率。
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.
从拥塞控制的角度来看，改变空间分辨率通常需要生成新的帧内编码帧，从而在输出业务模式中产生临时突发。帧速率变化的影响往往更微妙：将帧速率从高降低到低会导致稀疏的较大编码数据包，而不是许多密集的较小数据包。流量分布中的这种差异仍然可能影响拥塞控制的性能，特别是当媒体传输模块不调整传出分组的速度时。对不同帧速率和分辨率的研究留给了以后的工作。
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.
值得注意的是，统计模型和跟踪驱动模型各有其优缺点。这两种模型都相当容易实现。将统计模型的参数与实际编码器输出数据相匹配需要付出更大的努力。相比之下，跟踪驱动模型获取编码帧大小数据非常简单。一旦验证，统计模型通过简单地改变模型中的相应参数，在模拟广泛的编码器/内容行为方面更加灵活。在这方面，根据定义，跟踪驱动模型依赖于额外的数据收集工作来适应新的编解码器或视频内容。
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.
一般来说，跟踪驱动模型更适合模拟视频流量源在恒定目标速率附近波动的持续稳态行为。相比之下，统计模型更适合模拟视频流在瞬态中的行为，例如遇到突发速率变化时。也可以将这两种方法组合成一个混合模型。在这种情况下，稳态行为由稳态期间的轨迹驱动，瞬态行为由统计模型驱动。
transient +---------------+
state | Generate next |
+------>| K_d transient |
+-----------------+ / | frames |
R_v | Compare against | / +---------------+
------>| previous |/
| target bitrate |\
+-----------------+ \ +---------------+
\ | Generate next |
+------>| frame from |
steady | trace |
state +---------------+
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
图3：混合视频流量模型
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
如图3所示，如果所请求的目标速率R_v与先前的目标速率R_v显著不同，则视频业务模型在瞬态下运行；否则，它将在稳定状态下运行。在瞬态期间，统计模型总共生成K_d帧，导致一（1）个大小为K_B的大突发帧，然后是K_d-1个较小的帧。当在稳定状态下运行时，视频业务模型仅根据给定目标速率的跟踪驱动模型生成帧，同时根据指定的分布调制帧间隔
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.
统计模型。确定交通模型是否应在瞬态下运行的一个示例标准是速率变化是否超过先前目标速率的10%。最后，由于该模型遵循统计模型规定的瞬态行为，在发生重大速率变化时，该模型将遵循第5.1节中定义的时间阻尼机制，该机制由参数tau_v控制。
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].
本文档中描述的统计、跟踪驱动和混合模型已作为独立、平台独立的合成交通源模块实现。它可以很容易地集成到网络仿真平台（如[ns-2]和[ns-3]）以及使用真实网络的测试平台中。独立流量源模块作为开源实现在[Syncodecs]上提供。
This document has no IANA actions.
本文档没有IANA操作。
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.
本文档中描述的合成视频流量模型不会造成任何安全威胁。它们被设计来模拟真实的流量模式，以评估候选的基于RTP的拥塞控制算法，从而确保网络的稳定运行。建议在互联网上广泛部署之前，使用本文中介绍的视频流量模型测试候选算法。如果生成的合成流量通过互联网发送，则还需要对其进行拥塞控制。
[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>.
[RFC2119]Bradner，S.，“RFC中用于表示需求水平的关键词”，BCP 14，RFC 2119，DOI 10.17487/RFC2119，1997年3月<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>.
[RFC8174]Leiba，B.，“RFC 2119关键词中大写与小写的歧义”，BCP 14，RFC 8174，DOI 10.17487/RFC8174，2017年5月<https://www.rfc-editor.org/info/rfc8174>.
[H264] ITU-T, "Advanced video coding for generic audiovisual services", Recommendation H.264, April 2017, <https://www.itu.int/rec/T-REC-H.264>.
[H264]ITU-T，“通用视听服务的高级视频编码”，建议H.264，2017年4月<https://www.itu.int/rec/T-REC-H.264>.
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[ns-2]“网络模拟器-ns-2”，2015年12月<https://nsnam.sourceforge.net/wiki/index.php/ 用户信息>。
[ns-3] "NS-3 Network Simulator", <https://www.nsnam.org/>.
[ns-3]“ns-3网络模拟器”<https://www.nsnam.org/>.
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[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>.
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Authors' Addresses
作者地址
Xiaoqing Zhu Cisco Systems 12515 Research Blvd., Building 4 Austin, TX 78759 United States of America
朱晓青思科系统美国德克萨斯州奥斯汀研究大道12515号4号楼78759
Email: xiaoqzhu@cisco.com
Email: xiaoqzhu@cisco.com
Sergio Mena Cisco Systems EPFL, Quartier de l'Innovation, Batiment E Ecublens, Vaud 1015 Switzerland
Sergio Mena Cisco Systems EPFL，瑞士沃德1015埃库布伦斯巴蒂斯特创新酒店
Email: semena@cisco.com
Email: semena@cisco.com
Zaheduzzaman Sarker Ericsson AB Torshamnsgatan 23 Stockholm, SE 164 83 Sweden
扎赫杜扎曼萨克爱立信AB Torshamnsgatan 23斯德哥尔摩，东南164 83瑞典
Phone: +46 10 717 37 43
Email: zaheduzzaman.sarker@ericsson.com
Phone: +46 10 717 37 43
Email: zaheduzzaman.sarker@ericsson.com