Competitive on-line stream merging algorithms for media-on-demand
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
The dyadic stream merging algorithm
Journal of Algorithms
An 5-competitive on-line scheduler for merging video streams
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Competitive Analysis of On-line Stream Merging Algorithms
MFCS '02 Proceedings of the 27th International Symposium on Mathematical Foundations of Computer Science
Competitive on-line stream merging algorithms for media-on-demand
Journal of Algorithms - Special issue: Twelfth annual ACM-SIAM symposium on discrete algorithms
The Knuth-Yao quadrangle-inequality speedup is a consequence of total-monotonicity
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Bandwidth usage distribution of multimedia servers using Patching
Computer Networks: The International Journal of Computer and Telecommunications Networking
Hierarchical video patching with optimal server bandwidth
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Analysis and placement of storage capacity in large distributed video servers
Computer Communications
The Knuth-Yao quadrangle-inequality speedup is a consequence of total monotonicity
ACM Transactions on Algorithms (TALG)
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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 $\rho L/\log(\rho L)$ compared to the simple batching solution where L is the length of a stream and $\rho\le 1$ 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.