Soft ARQ for layered streaming media

A growing and important class of traffic in the Internet is so-called `streaming media,' in which a server transmits a packetized multimedia signal to a receiver that buffers the packets for playback. This playback buffer, if adequately sized, counteracts the adverse impact of delay jitter and reordering suffered by packets as they traverse the network, and if large enough also allows lost packets to be retransmitted before their playback deadline expires. We call this framework for retransmitting lost streaming-media packets `soft ARQ' since it represents a relaxed form of Automatic Repeat reQuest (ARQ). While state-of-the-art media servers employ such strategies, no work to date has proposed an optimal strategy for delay-constrained retransmissions of streaming media-specifically, one which determines what is the optimal packet to transmit at any given point in time. In this paper, we address this issue and present a framework for streaming media retransmission based on layered media representations, in which a signal is decomposed into a discrete number of layers and each successive layer provides enhanced quality. In our approach, the source chooses between transmitting (1) newer but critical coarse information (e.g., a first approximation of the media signal) and (2) older but less important refinement reformation (e.g., added details) using a decision process that minimizes the expected signal distortion at the receiver. To arrive at the proper mix of these two extreme strategies, we derive an optimal strategy for transmitting layered data over a binary erasure channel with instantaneous feedback. To provide a quantitative performance comparison of different transmission policies, we conduct a Markov-chain analysis, which shows that the best transmission policy is time-invariant and thus does not change as the frames' layers approach their expiration times

Методы оптимизации качества потоковых видеоизображений при передаче по сети интернет

Работа посвящена вопросу оптимизации качества видеоизображения при передаче потокового видео по сети Интернет. Рассматривается случай гарантированной доставки пакетов. При этом учитываются такие характеристики сети как ограниченная и меняющаяся пропускная способность. Построена аналитическая модель, учитывающая дерево зависимостей между элементами данных видеопоследовательности. Рассмотрены подходы ее решения, основанные на составлении расписания передачи пакетов.The article is dedicated to questions of optimizing video quality in case of streaming over Internet. The packet delivery is considered to be assured. Limited and time-varying bandwidth of the Internet is considered. Analytic model that takes into account tree of dependencies between data elements inside video is built. Approaches to its resolving that based on packets scheduling are considered

Optimized protection of streaming media authenticity

Ph.DDOCTOR OF PHILOSOPH

CROSS-LAYER DISTORTION CONTROL FOR DELAY SENSITIVE SOURCES

The existence of layers in the traditional network architecture facilitates the network design by modularizing it and thus enabling isolated design of the different layers. However, due to the inherent coupling and interactions between these layers, their isolated design often leads to suboptimal performance. On the other hand, the recent popularity of realtime multimedia applications has pushed the boundaries of layered designs. Cross-layer network design provides opportunities for significant performance improvement by selectively exploiting the interactions between layers, and therefore has attracted a lot of attention in recent years. Realtime multimedia applications are characterized by their delay-sensitivity and distortion-tolerance. The focus of this thesis is on Source Coding for Delay-Sensitive Distortion-Tolerant data. In particular, we notice that even though using longer descriptions for source symbols results in smaller distortion for each particular symbol, it also increases the delay experienced in the network, which in turn causes information loss for a delay-sensitive source, and therefore, increases the overall distortion of the received message. In this thesis we investigate this trade-off across the layers by considering two different problems. In the first problem, we focus on a single source-destination pair to exploit the interconnection between Source Coding, traditionally a presentation layer component, and Parallel Routing, a network layer issue. We use a Distortion Measure that combines signal reconstruction fidelity with network delay. We minimize this measure by jointly choosing the Encoder Parameters and the Routing Parameters. We look at both single-description and multiple-description codings and perform numerical optimizations that provide insight into design tradeoffs which can be exploited in more complex settings. We then investigate the problem of finding minimum-distortion policies for streaming delay-sensitive distortion-tolerant data. We use a cross-layer design which exploits the coupling between the presentation layer and the transport and link layers. We find an optimum transmission policy for error-free channels, which is independent of the particular form of the distortion function when it is convex and decreasing. For a packet-erasure channel, we find computationally efficient heuristic policies which have near optimal performance

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