A hybrid architecture for cost-effective on-demand media streaming
Computer Networks: The International Journal of Computer and Telecommunications Networking
Statistical buffering for streaming media data access in a mobile environment
Proceedings of the 2006 ACM symposium on Applied computing
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Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
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ACM Transactions on the Web (TWEB)
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A hybrid peer-to-peer media streaming
GCC'05 Proceedings of the 4th international conference on Grid and Cooperative Computing
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PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
AngelCast: cloud-based peer-assisted live streaming using optimized multi-tree construction
Proceedings of the 3rd Multimedia Systems Conference
Mobile learning support with statistical inference-based cache management
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
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Internet streaming applications are affected by adverse network conditions such as high packet loss rates and long delays. This paper aims at mitigating such effects by leveraging the availability of client-side caching proxies. We present a novel caching architecture and associated cache management algorithms that turn edge caches into accelerators of streaming media delivery. A salient feature of our caching algorithms is that they allow partial caching of streaming media objects and joint delivery of content from caches and origin servers. The caching algorithms we propose are both network-aware and stream-aware; they take into account the popularity of streaming media objects, their bit-rate requirements, and the available bandwidth between clients and servers. Using realistic models of Internet bandwidth derived from proxy cache logs and measured over real Internet paths, we have conducted simulations to evaluate the performance of various cache management alternatives. Our experiments demonstrate that network-aware caching algorithms can significantly reduce service delay and improve overall stream quality. Our experiments also show that partial caching is particularly effective when bandwidth variability is not very high.