In this paper we investigate adaptive streaming of stored fine-grained scalable video over a TCP-friendly connection. The goal is to develop low-complexity yet high-performing schemes that adequately adapt to the short-and long-term variations in available bandwidth. We first present a novel framework for low-complexity streaming of fine-grained scalable video over a TCP-friendly connection. In the context of this scheme, and under the assumption of complete knowledge of bandwidth evolution, we derive an optimal policy for a criterion that involves both image quality and quality variability during playback. Based on this ideal optimal policy, we develop a real-time heuristic to stream fine-grained scalable video over the Internet, and we study its performance using real Internet traces. We find that our heuristic policy performs almost as well as the ideal optimal policy for a wide-range of bandwidth scenarios and when run over ordinary TCP the policy is essentially as good as when running the policy over popular TCP-friendly algorithms.

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We study the problem of how to stream layered video (live and stored) over a lossy packet network in order to optimize the video quality that is rendered at the receiver. We present a unified framework that combines scheduling, FEC error protection, and decoder error concealment. In the context of the unified framework, we study both the case of a channel with perfect state information and the case of a channel with imperfect state information (delayed or lost feedback). We adapt the theory of infinite-horizon, average-reward Markov decision processes (MDPs) with average-cost constraints to the problem. Based on simulations with MPEG-4 FGS video, we show that (1) optimizing together scheduling, FEC error correction and error concealment improves performance significantly and (2) policies with static error protection give near-optimal performance. We also find that degradations in quality for a channel with imperfect state information are small; thus our MDP approach is suitable for networks with long end-to-end delays.

SUMMARYFine granularity scalability (FGS), a new coding technique that has recently been added to the MPEG-4 video coding standard, allows for the flexible scaling of each individual video frame at very fine granularity. This flexibility makes FGS video very well suited for rate-distortion optimized streaming mechanisms, which minimize the distortion (i.e. maximize the quality) of the streamed video by transmitting the optimal number of bits for each individual frame. The per-frame optimization of the transmission schedule, however, puts a significant computational burden on video servers and intermediate streaming gateways. In this paper we investigate the rate-distortion optimized streaming at different video frame aggregation levels. We find that compared to the optimization for each individual video frame, optimization at the level of video scenes reduces the computational effort dramatically, while reducing the video quality only very slightly.