Packetized Media Streaming with Comprehensive Exploitation of Feedback Information

This paper addresses the problem of streaming packetized media over a lossy packet network, with sender-driven (re)transmission using acknowledgement feedback. The different transmission scenarios associated to a group of interdependent media data units are abstracted in terms of a finite alphabet of policies, for each single data unit. A rate-distortion optimized markovian framework is proposed, which supports the use of comprehensive feedback information. Contrarily to previous works in rate-distortion optimized streaming, whose transmission policies definitions do not take into account the feedback expected for other data units, our framework considers all the acknowledgment packets in defining the streaming policy of a single data unit. More specifically, the notion of master and slave data unit is introduced, to define dependent streaming policies between media packets; the policy adopted to transmit a slave data unit becomes dependent on the acknowledgments received about its masters. One of the main contributions of our work is to propose a methodology that limits the space of dependent policies for the RD optimized streaming strategy. A number of rules are formulated to select a set of relevant master/slave relationships, defined as the dependencies that are likely to bring RD performance gain in the streaming system. These rules provide a limited complexity solution to the rate-distortion optimized streaming problem, with comprehensive use of feedback information. Based on extensive simulations, we conclude that (i) the proposed set of relevant dependent policies achieves close to optimal performance, while being computationally tractable, and (ii) the benefit of dependent policies is driven by the relative sizes and importance of interdependent data units. Our simulations demonstrate that dependent streaming policies can perform significantly better than independent streaming strategies, especially for cases where some media data units bring a relatively large gain in distortion, in comparison with other data units they depend on for correct decoding. We observe however that the benefit becomes marginal when the gain in distortion per unit of rate decreases along the media decoding dependency path. Since such a trend characterizes most conventional scalable coders, the implementation of dependent policies can reasonably be ruled out in these specific cases

Streaming Video over HTTP with Consistent Quality

In conventional HTTP-based adaptive streaming (HAS), a video source is encoded at multiple levels of constant bitrate representations, and a client makes its representation selections according to the measured network bandwidth. While greatly simplifying adaptation to the varying network conditions, this strategy is not the best for optimizing the video quality experienced by end users. Quality fluctuation can be reduced if the natural variability of video content is taken into consideration. In this work, we study the design of a client rate adaptation algorithm to yield consistent video quality. We assume that clients have visibility into incoming video within a finite horizon. We also take advantage of the client-side video buffer, by using it as a breathing room for not only network bandwidth variability, but also video bitrate variability. The challenge, however, lies in how to balance these two variabilities to yield consistent video quality without risking a buffer underrun. We propose an optimization solution that uses an online algorithm to adapt the video bitrate step-by-step, while applying dynamic programming at each step. We incorporate our solution into PANDA -- a practical rate adaptation algorithm designed for HAS deployment at scale.Comment: Refined version submitted to ACM Multimedia Systems Conference (MMSys), 201

Low-Complexity Adaptive Streaming via Optimized A Priori Media Pruning

Abstract — Source pruning is performed whenever the data rate of the compressed source exceeds the available communication or storage resources. In this paper, we propose a framework for rate-distortion optimized pruning of a video source. The framework selects which packets, if any, from the compressed representation of the source should be discarded so that the data rate of the pruned source is adjusted accordingly, while the resulting reconstruction distortion is minimized. The framework relies on a rate-distortion preamble that is created at compression time for the video source and that comprises the video packets ’ sizes, interdependencies and distortion importances. As one application of the pruning framework, we design a low-complexity rate-distortion optimized ARQ scheme for video streaming. In the experiments, we examine the performance of the pruning framework depending on the employed distortion model that describes the effect of packet interdependencies on the reconstruction quality. In addition, our experimental results show that the enhanced ARQ technique provides significant performance gains over a conventional system for video streaming that does not take into account the different importance of the individual video packets. These gains are achieved without an increase in packet scheduling complexity, which makes the proposed technique suitable for online R-D optimized streaming. I