Approximation algorithms for facility location problems (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
IEEE/ACM Transactions on Networking (TON)
Analysis of educational media server workloads
NOSSDAV '01 Proceedings of the 11th international workshop on Network and operating systems support for digital audio and video
GISMO: a Generator of Internet Streaming Media Objects and workloads
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A hierarchical characterization of a live streaming media workload
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Hybrid Search Algorithms for P2P Media Streaming Distribution in Ad Hoc Networks
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
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EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
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Media streaming in wireless ad hoc networks is challenging due to the stringent resource restrictions and the decentralized architecture. To support long and high-quality streams, one viable approach is divide-and-conquer. A media stream is partitioned into segments, and then the segments are replicated in a network and served in a peer-to-peer fashion. It alleviates resource requirements on light-weight devices, improves load balancing, and provides an opportunity for fine-grain replication, among others. This paper describes a peer-to-peer service model using this approach, and in particular, studies replication strategies for the segments. We exploit topological properties of the underlying networks, and exploit correlation of streaming access. Several strategies are described and evaluated. A novel strategy uses adaptive and selective replication. It infers end-host clustering from hop-distance, and selectively replicates media segments to avoid starving any of them. Preliminary simulation study demonstrates its effectiveness in minimizing the cost to discover and retrieve media data.