Search

The recently developed H.264/AVC video codec with Scalable Video Coding (SVC) extension, compresses non-scalable (single-layer) and scalable video significantly more efficiently than MPEG-4 Part 2. Since the traffic characteristics of encoded video have a significant impact on its network transport, we examine the bit rate-distortion and bit rate variability-distortion performance of single-layer video traffic of the H.264/AVC codec and SVC extension using long CIF resolution videos. We also compare the traffic characteristics of the hierarchical B frames (SVC) versus classical B frames. In addition, we examine the impact of frame size smoothing on the video traffic to mitigate the effect of bit rate variabilities. We find that compared to MPEG-4 Part 2, the H.264/AVC codec and SVC extension achieve lower average bit rates at the expense of significantly increased traffic variabilities that remain at a high level even with smoothing. Through simulations we investigate the implications of this increase in rate variability on (i) frame losses when transmitting a single video, and (ii) on a bufferless statistical multiplexing scenario with restricted link capacity and information loss. We find increased frame losses, and rate-distortion/ rate-variability/encoding complexity tradeoffs. We conclude that solely assessing bit rate-distortion improvements of video encoder technologies is not sufficient to predict the performance in specific networked application scenarios. Index Terms--Frame loss ratio, H.264/AVC, hierarchical B frames, rate variability-distortion (VD), rate-distortion (RD), single-layer video, statistical multiplexing, SVC, video quality, video traffic.

The real-time streaming of bursty continuous media, such as variable-bit rate encoded video, to buffered clients over networks can be made more efficient by collaboratively prefetching parts of the ongoing streams into the client buffers. The existing collaborative prefetching schemes have been developed for discrete time models, where scheduling decisions for all ongoing streams are typically made for one frame period at a time. This leads to inefficiencies as the network bandwidth is not utilized for some duration at the end of the frame period when no video frame 'fits' into the remaining transmission capacity in the schedule. To overcome this inefficiency, we conduct in this paper an extensive study of collaborative prefetching in a continuous-time model. In the continuous-time model, video frames are transmitted continuously across frame periods, while making sure that frames are only transmitted if they meet their discrete playout deadlines. We specify a generic framework for continuous-time collaborative prefetching and a wide array of priority functions to be used for making scheduling decisions within the framework. We conduct an algorithm-theoretic study of the resulting continuous-time prefetching algorithms and evaluate their fairness and starvation probability performance through simulations. We find that the continuous-time prefetching algorithms give favorable fairness and starvation probability performance. Index Terms--Client buffer, continuous media, continuous-time, fairness, playback starvation, prefetching, prerecorded media, traffic smoothing, video streaming.

We examine and model the bit rate variability-distortion (VD) curve of -4 Part 2 variable bit rate (VBR) video encodings. The VD curve has important applications for evaluating the statistical multiplexing of streaming video. We show that the concave VD curve shape at high compression ratios, or equivalently large quantization scales, is influenced by both the texture and the motion information. Based on this insight, we first develop a general VD curve model by analytically expressing the VD curve in terms of elementary statistics (mean, variance, covariance) of the numbers of motion and texture coding bits. In a second step we develop and validate linear and quadratic models for the elementary texture and motion bit statistics, whereby the model parameters are obtained from only two sample encodings. The texture and motion bit models are then employed in our general VD curve model. This work extends our previous work on piecewise models of the VD curve. The texture and motion based VD model obtained from two sample encodings has comparable accuracy to a piece-wise model based on three sample encodings. In addition, the texture and motion based VD model provides fundamental insights into how texture and motion affect traffic variability. Index Terms--Communication systems, -4 Part 2, rate variability-distortion, statistical multiplexing, variable bit rate, video coding, video content, video quality, video streaming, video traffic.

Encoded video is expected to contribute a significant portion of the load on future communication systems and networks, which often employ statistical multiplexing. In such systems, the number of video streams that can be supported depends both on the mean bit rate as well as bit rate variability of the video streams. At the same time, the utility (revenue) earned from video streaming depends both on the number of supported video streams as well as their quality level. In this paper we examine the interplay between video quality, traffic variability, and utility for open-loop encoded video. We introduce the rate variability-distortion (VD) curve which relates the bit rate variability to the quality level of an encoded video. We find that the VD curve generally exhibits a characteristic 'hump' behavior of first increasing, peaking, and subsequently decreasing variability for increasing quality. We examine the impact of video content characteristics, encoding parameters, and traffic smoothing on the VD behavior. We describe a methodology for assessing (i) the set of the video streams that can be supported with a statistical quality of service requirement, and (ii) the utility earned from video streaming over a link. This methodology is based on the rate-distortion and rate variability-distortion characteristics of the videos. We find that the statistical multiplexing gain and the utility as a function of the video quality level typically exhibit a 'hump' similar to the VD curve. Index Terms--Network utility, statistical multiplexing, variable bit rate video, video content, video quality, video streaming, video traffic.

Bursty continuous media streams with periodic playout deadlines (e.g., VBR-encoded video) are expected to account for a large portion of the traffic in the future Internet. By prefetching parts of ongoing streams into client buffers these bursty streams can be more efficiently accommodated in packet-switched networks. In this paper we develop a modular algorithm-theoretic framework for the fair and efficient transmission of continuous media over a bottleneck link. We divide the problem into the two subproblems of (i) assuring fairness, and (ii) efficiently utilizing the available link capacity. We develop and analyze algorithm modules for these two subproblems. Specifically, we devise a bin packing algorithm for subproblem (i), and a 'layered prefetching' algorithm for subproblem (ii). Our simulation results indicate that the combination of these two algorithm modules compares favorably with existing monolithic solutions. This demonstrates the competitiveness of the decoupled modular algorithm framework, which provides a foundation for the development of refined algorithms for fair and efficient prefetching. Index Terms--Client buffer, continuous media, fairness, playback starvation, prefetching, prerecorded media, video streaming.

The real-time streaming of bursty continuous media, such as variable-bit rate encoded video, to buffered clients over networks can be made more efficient by collaboratively prefetching parts of the ongoing streams into the client buffers. The existing collaborative prefetching schemes have been developed for discrete time models, where scheduling decisions for all ongoing streams are typically made for one frame period at a time. This leads to inefficiencies as the network bandwidth is not utilized for some duration at the end of the frame period when no video frame "fits" into the remaining transmission capacity in the schedule. To overcome this inefficiency, we conduct in this paper an extensive study of collaborative prefetching in a continuous-time model. In the continuous-time model, video frames are transmitted continuously across frame periods, while making sure that frames are only transmitted if they meet their discrete playout deadlines. We specify a generic framework for continuous-time collaborative prefetching and a wide array of priority functions to be used for making scheduling decisions within the framework. We conduct an algorithm-theoretic study of the resulting continuous-time prefetching algorithms and evaluate their fairness and starvation probability performance through simulations. We find that the continuous-time prefetching algorithms give favorable fairness and starvation probability performance. [ABSTRACT FROM AUTHOR]