Computer Science ›› 2015, Vol. 42 ›› Issue (7): 258-261.doi: 10.11896/j.issn.1002-137X.2015.07.055

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Video Prefetching Strategy Based on Congestion Finding with Reinforcement Learning in P2P VOD Networks

SHEN Xiang-jun, YAO Yin and ZHA Zheng-jun   

  • Online:2018-11-14 Published:2018-11-14

Abstract: A proper video prefetching strategy in P2P (peer to peer) VOD (Video on Demand) networks can effectively solve such circumstances as waiting time for buffering is too long and server is overloaded.While the existing video prefetching methods in P2P networks just consider video content finding but ignore monitoring nodes status,which makes poor video play when the network congestions occur.This paper presented a video prefetching strategy based on congestion finding with reinforcement learning in P2P VOD network.Through monitoring nodes status of congestion,bandwidth and other parameters,the Q-learning learning algorithm was used to evaluate network nodes,which can guide nodes selection and reduce the video prefetching from congested nodes.Simulation results show that the proposed method can make video playback smooth.It can also avoid too long waiting time when congestions happen and improve the efficiency of video playback.

Key words: P2P network,Video prefetching,Congestion finding,Reinforcement learning

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