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Efficient algorithms for optimal stream merging for media-on-demand

机译:用于按需媒体的最佳流合并的高效算法

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We address the problem of designing optimal off-line algorithms that minimize the required bandwidth for media-on-demand systems that use stream merging. We concentrate on the case where clients can receive two media streams simultaneously and can buffer up to half of a full stream. We construct an O(nm) optimal algorithm for n arbitrary time arrivals of clients, where m is the average number of arrivals in an interval of a stream length. We then show how to adopt our algorithm to be optimal even if clients have a limited size buffer. The complexity remains the same.We also prove that using stream merging may reduce the required bandwidth by a factor of order rhoL/log(rhoL) compared to the simple batching solution where L is the length of a stream and rholess than or equal to1 is the density in time of all the n arrivals. On the other hand, we show that the bandwidth required when clients can receive an unbounded number of streams simultaneously is always at least 1/2 the bandwidth required when clients are limited to receiving at most two streams.
机译:我们解决了设计最佳离线算法的问题,该算法可以最小化使用流合并的按需媒体系统所需的带宽。我们专注于客户端可以同时接收两个媒体流并且最多可以缓冲整个流一半的情况。我们为n个任意时间到达的客户机构造一个O(nm)最优算法,其中m是流长度间隔中的平均到达数。然后,我们展示了即使客户端的缓冲区大小有限,如何采用我们的算法也能达到最佳效果。复杂度保持不变。我们还证明,与简单批处理解决方案(其中L是流的长度且rholess小于或等于1是)的简单批处理解决方案相比,使用流合并可以将所需带宽减少rhoL / log(rhoL)倍。所有n次到达的时间密度。另一方面,我们表明,当客户端可以同时接收无限制数量的流时,所需的带宽始终至少是将客户端限制为最多接收两个流时所需的带宽的1/2。

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