Abstract-The network transport of 3D video, which contains 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. We examine the average bitrate-distortion (RD) and bitrate variability-distortion (VD) 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 3D videos in 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.

Abstract-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.

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Abstract-We present different video traffic models for H.264 variable bit rate (VBR) videos. We propose our models on top of the recent unified traffic model developed by Dai et al. [1], which presents a frame-level hybrid framework for modeling MPEG-4 and H.264 multi-layer VBR video traffic. We exploit the hierarchical predication structure inherent in H.264 for intraGoP (group of pictures) analysis. We model the children frames by considering various combinations of the correlation between the parent frames in the prediction structure. Our simulations show that modeling using the hierarchical prediction structure indeed improves capturing the statistical features of the videos and prediction of network performance, without an increase in the complexity as compared to the unified traffic model by Dai et al. [1], which was shown earlier to be better than previous traffic models.