Soft ARQ for Layered Streaming Media

Abstract

Abstract.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 information (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.

A Markov Decision Model for Adaptive Scheduling of Stored Scalable Videos

“…Adaptive video data scheduling is an important topic of research [3]- [12]. In [3], adaptive video transmission over a packet erasure channel was studied by modeling the buffer state as a controlled Markov chain.…”

“…In [3], adaptive video transmission over a packet erasure channel was studied by modeling the buffer state as a controlled Markov chain. In [4], an average-rate constrained MDP formulation was proposed to optimize the quality of error concealed videos at the receiver.…”

“…Because the transmission rate is finite, the number of states in S W which can be reached 3 This assumption is empirically true. We calculated the correlation coef- from S W in one step is also finite.…”

Multiple Distortion Measures for Packetized Scalable Media

“…Optimized packet scheduling is an important problem and has garnered quite a lot of attention. Some of the early work includes [16]- [19] and [20], [21] for embedded scheduling. We refer the reader to the preceding references and the references therein for more background on packet scheduling.…”

Optimal packet scheduling and rate control for video streaming

“…al. 4 sought the optimal solution using Markov Decision Process (MDP), however it is far from practicality. Later in Chou's seminal paper 5 a very flexible and practical MDP framework based on the idea of transmitting packets in a rate distortion optimized (RaDiO) manner that decides which packet to transmit in order to minimize a Lagrangian cost function that is calculated by using rate and distortion information, channel statistics, packet deadlines and transmission history.…”