Video traffic is widely expected to account for a large portion of the traffic in future wireline and wireless networks, as multimedia applications are becoming increasingly popular. Consequently, the performance evaluation of networking architectures, protocols, and mechanisms for video traffic becomes increasingly important. Video traces, which give the sizes, deadlines, and qualities of the individual video frames in a video sequence, have been emerging as convenient video characterizations for networking studies. In this tutorial we give an introduction to the use of video traces in networking studies. First we give a brief overview of digital video and its encoding and playout. Then we present a library of traces of single- and two-layer encoded video. We discuss the statistical properties of the traces and the resulting implications for the transport of video over networks. Finally we discuss the factors that need to be considered when using video traces in network performance evaluations. In particular, we introduce performance metrics that quantify the quality of the delivered video. We outline a procedure for generating video load for network simulations from the traces, and discuss how to meaningfully analyze the outcomes of these simulations.

The performance evaluation of video transport mechanisms becomes increasingly important as encoded video accounts for growing portions of the network traffic. Compared to the widely studied MPEG-4 encoded video, the recently adopted H.264 video coding standards include novel mechanisms, such as hierarchical B frame prediction structures and highly efficient quality scalable coding, that have important implications for network transport. This tutorial introduces a trace-based evaluation methodology for the network transport of H.264 encoded video. We first give an overview of H.264 video coding, and then present the trace structures for capturing the characteristics of H.264 encoded video. We give an overview of the typical video traffic and quality characteristics of H.264 encoded video. Finally, we explain how to account for the H.264 specific coding mechanisms, such as hierarchical B frames, in networking studies.

Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC.

The network transport of 3-D video, which contains two views of a video scene, poses significant challenges due to the increased video data, compared to a conventional single-view video. Addressing these challenges requires a thorough understanding of the traffic and multiplexing characteristics of the different representation formats of a 3-D video. We examine the average bitrate-distortion (RD) and bitrate variability-distortion characteristics of three main representation formats. Specifically, we compare multiview video (MV) representation and encoding, frame sequential (FS) representation, and side-by-side (SBS) representation, whereby conventional single-view encoding is employed for the FS and SBS representations. Our results for long 3-D videos in the full HD format indicate that the MV representation and encoding achieves the highest RD efficiency, while exhibiting the highest bitrate variabilities. We examine the impact of these bitrate variabilities on network transport through extensive statistical multiplexing simulations. We find that when multiplexing a small number of streams, the MV and FS representations require the same bandwidth. However, when multiplexing a large number of streams or smoothing traffic, the MV representation and encoding reduces the bandwidth requirement relative to the FS representation.

The scalable video coding (SVC) extension of the H.264/AVC video coding standard provides two mechanisms, namely coarse grain scalability (CGS) and medium grain scalability (MGS), for quality scalable video encoding, which varies the fidelity (signal-to-noise ratio) of the encoded video stream. As H.264/AVC and its SVC extension are expected to become widely adopted for the network transport of video, it is important to thoroughly study their network traffic characteristics, including the bit rate variability. In this paper, we report on a large-scale study of the rate-distortion (RD) and rate variability-distortion (VD) characteristics of CGS and MGS. We found that CGS achieves low bit rate overheads in the 10–30% range compared to H.264 SVC single-layer encodings only for encodings with a total of up to three quality levels; more quality levels result in substantially higher overheads. The traffic variabilities of CGS are generally lower than for single-layer streams. We found that in the low to mid range of the MGS quality scalability, MGS can achieve the same or even slightly higher RD efficiency than corresponding single-layer encoding; toward the upper end of the MGS quality scalability range the RD efficiency drops off significantly. MGS layer extraction following the hierarchical B frame structure gives nearly as high RD performance as RD-optimized extraction. In the range of high RD efficiency, MGS streams have significantly higher traffic variabilities than single-layer streams at the frame time scale. At the group-of-pictures (GoP) time scale, MGS has similar or lower levels of traffic variability compared to single-layer streams. Generally, MGS layer extraction over the time horizon of individual GoPs gives significantly lower traffic variability than extraction over the time horizon of the full video sequence.