Viewing experience optimization based on adaptive requesting window adjustment in peer-to-peer media streaming

Authors:
Hao Ye;Kaiping Xue;Peilin Hong;Hancheng Lu
Affiliations:
MOE-MS Key Laboratory of Multimedia Computing and Communication and The Information Network Lab of EEIS Department, University of Science and Technology of China (USTC), Hefei 230027, China and Th ...;MOE-MS Key Laboratory of Multimedia Computing and Communication and The Information Network Lab of EEIS Department, University of Science and Technology of China (USTC), Hefei 230027, China and Th ...;The Information Network Lab of EEIS Department, University of Science and Technology of China (USTC), Hefei 230027, China;The Information Network Lab of EEIS Department, University of Science and Technology of China (USTC), Hefei 230027, China
Venue:
Computers and Electrical Engineering
Year:
2011

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Abstract

In the last few years, great achievements have been made in the field of peer-to-peer (P2P) streaming system. In mesh-based P2P streaming system, retrieving data from multiple parents simultaneously improves resilience of the streaming service. However, it also brings in playback lags. To improve users' viewing experiences, we should consider both playback lags and resilience of the streaming service. However, it seems there is a natural trade-off between these two goals. In this paper we propose a viewing experience optimization algorithm which adjusts the position of the requesting window adaptively based on different requesting strategies. The novelty of the proposed method is that it utilizes heterogeneous real-time demands for different genres of videos, and can adjust requesting strategies adaptively. Under the condition of guaranteeing basic QoS (Quality of Service), the algorithm achieves a balance between playback lags and resilience. Finally, experiment results show the satisfactory performance of the proposed method.