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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.

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Abstract-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.