Traffic characterization and modeling of H.264 scalable & multi view encoded video

Present day Internet Protocol (IP) based video transport and dissemination systems are heterogeneous in that they differ in network bandwidth, display resolutions and processing capabilities. One important objective in such an environment is the flexible adaptation of once-encoded content and to achieve this, one popular method is the scalable video coding (SVC) technique. The SVC extension of the H.264/AVC standard has higher compression efficiency when compared to the previous scalable video standards. The network transport of 3D video, which is obtained by superimposing two views of a video scene, poses significant challenges due to the increased video data compared to conventional single-view video. Addressing these challenges requires a thorough understanding of the traffic and multiplexing characteristics of the different representation formats of 3D video. In this study, H.264 quality scalability and multiview representation formats are examined. As H.264/AVC, it's SVC and multiview extensions 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. Primarily the focus is on the SVC amendment of the H.264/AVC standard, with particular focus on Coarse-Grain Scalability (CGS) and Medium-Grain Scalability (MGS). In this study, we report on a large-scale study of the rate-distortion (RD) and rate variability-distortion (VD) characteristics of CGS and MGS. We also examine the RD and VD characteristics of three main multiview (3D) 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. As a last step, we also examine Video traffic modeling which plays a major part in network traffic analysis. It is imperative to network design and simulation, providing Quality of Service (QoS) to network applications, besides providing insights into the coding process and structure of video sequences. We propose our models on top of the recent unified traffic model developed by Dai et al. [1], for modeling MPEG-4 and H.264 VBR video traffic. We exploit the hierarchical predication structure inherent in H.264 for intra-GoP (group of pictures) analysis.