Competitive algorithms for server problems
Journal of Algorithms
Page migration algorithms using work functions
Journal of Algorithms
A caching and streaming framework for mulitmedia
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Segment-based proxy caching of multimedia streams
Proceedings of the 10th international conference on World Wide Web
Theory and Applications of Problem Solving
Theory and Applications of Problem Solving
WebCompanion: A Friendly Client-Side Web Prefetching Agent
IEEE Transactions on Knowledge and Data Engineering
Investigation of Cache Maintenance Strategies for Multi-cell Environments
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Performance Evaluation of Transcoding-Enabled Streaming Media Caching System
MDM '03 Proceedings of the 4th International Conference on Mobile Data Management
Accelerating Internet Streaming Media Delivery using Network-Aware Partial Caching
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Measurement and analysis of a streaming-media workload
USITS'01 Proceedings of the 3rd conference on USENIX Symposium on Internet Technologies and Systems - Volume 3
Headlight prefetching for mobile media streaming
MobiDE '07 Proceedings of the 6th ACM international workshop on Data engineering for wireless and mobile access
An evolution-based cache scheme for scalable mobile data access
Proceedings of the 2nd international conference on Scalable information systems
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Streaming media (e.g., music or video) data access has been a research problem over the past few years, and the problem becomes tougher when the clients are mobile devices whose limited storage spaces prevent the clients from holding a large cache. A practical solution for the cellular system is to buffer the streaming data on the base stations, serving as the "cache" to the mobile devices. However, when mobile devices move from one cell to another, the cached data should also be migrated to the corresponding base station in order that users can view the media smoothly. When the number of requests increases, stations may face heavy data migration and storage burden. In this paper, we propose a statistical buffering mechanism by adapting SAA search which makes use of prior knowledge (statistical data) to predict the trend of user movement among cells. Experimental studies show that, with an acceptable complexity, our algorithms can obtain good performance on buffering streaming media data.