Distributed prefetching scheme for random seek support in peer-to-peer streaming applications
Proceedings of the ACM workshop on Advances in peer-to-peer multimedia streaming
Challenges, design and analysis of a large-scale p2p-vod system
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Optimizing the Throughput of Data-Driven Peer-to-Peer Streaming
IEEE Transactions on Parallel and Distributed Systems
Analyzing patterns of user content generation in online social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Coolstreaming: Design, Theory, and Practice
IEEE Transactions on Multimedia
Propagation-based social-aware replication for social video contents
Proceedings of the 20th ACM international conference on Multimedia
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Online social network has emerged as the most popular approach for people to directly access multimedia contents. Among these contents, video sharing is a challenging task due to the demand on a large amount of uplink bandwidth at the dedicated server. We leverage a P2P paradigm to alleviate the server to distribute shared videos. By investigating traces obtained from a popular online social network in China, we observe that users' preferences can be predicted. We design a user preference guided prefetching strategy to reduce video startup delays, enabling smooth playback. Simulation experiments show that our design achieves high prefetch accuracy and short startup delay with conservative storage and bandwidth capacities at peers.