Comparison of stream merging algorithms for media-on-demand

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Abstract
Stream merging is a technique for efficiently delivering popular media-on-demand using multicast and client buffers. The basic idea is that clients may simultaneously receive more data than they need for playback and store parts of the transmission in their buffers to be played back later. It was shown that with these additional capabilities, the bandwidth requirements for servers are dramatically reduced compared with traditional unicast systems and multicast systems that use only batching. Recently, several algorithms for stream merging have been proposed, and we perform a comprehensive comparison in this paper. We present the differences in philosophy and mechanics among the various algorithms, and illustrate the trade-offs between their system complexity and performance. We measure performance in average, maximum, and time-varying server bandwidth usage under different assumptions for the client request patterns. The result of this study is a deeper understanding of the system complexity and performance trade-offs for the various algorithms.

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