Playback Policies for Live and On-Demand P2P Video Streaming

Peer-to-peer (P2P) has become a popular mechanism for video distribution over the Internet, by allowing users to collaborate on locating and exchanging video blocks. The approach LiveShift supports further collaboration by enabling storage and a later redistribution of received blocks, thus, enabling time shifting and video-on-demand in an integrated manner. Video blocks, however, are not always downloaded quickly enough to be played back without interruptions. In such situations, the playback policy defines whether peers (a) stall the playback, waiting for blocks to be found and downloaded, or (b) skip them, losing information. Thus, for the fist time this paper investigates in a reproducible manner playback policies for P2P video streaming systems. A survey on currently-used playback policies shows that existing playback policies, required by any streaming system, have been defined almost arbitrarily, with a minimal scientific methodology applied. Based on this survey and on major characteristics of video streaming, a set of five distinct playback policies is formalized and implemented in LiveShift. Comparative evaluations outline the behavior of those policies under both under- and over-provisioned networks with respect to the playback lag experienced by users, the share of skipped blocks, and the share of sessions that fail. Finally, playback policies with most suitable characteristics for either live or on-demand scenarios are derived.

[1]  Keith W. Ross,et al.  A Measurement Study of a Large-Scale P2P IPTV System , 2007, IEEE Transactions on Multimedia.

[2]  David Hausheer,et al.  LiveShift: mesh-pull P2P live and time-shifted video streaming , 2010 .

[3]  Rakesh Kumar,et al.  Stochastic Fluid Theory for P2P Streaming Systems , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[4]  Jianping Fan,et al.  Incorporating feature hierarchy and boosting to achieve more effective classifier training and concept-oriented video summarization and skimming , 2008, TOMCCAP.

[5]  Yang Guo,et al.  Is Random Scheduling Sufficient in P2P Video Streaming? , 2008, 2008 The 28th International Conference on Distributed Computing Systems.

[6]  Mario Gerla,et al.  Will IPTV ride the peer-to-peer stream? [Peer-to-Peer Multimedia Streaming] , 2007, IEEE Communications Magazine.

[7]  J.J.D. Mol,et al.  Free-riding Resilient Video Streaming in Peer-to-Peer Networks , 2010 .

[8]  Krishna P. Gummadi,et al.  King: estimating latency between arbitrary internet end hosts , 2002, IMW '02.

[9]  Johan A. Pouwelse,et al.  The Design and Deployment of a BitTorrent Live Video Streaming Solution , 2009, 2009 11th IEEE International Symposium on Multimedia.

[10]  Hai Jin,et al.  GridCast: Improving peer sharing for P2P VoD , 2008, TOMCCAP.

[11]  Reza Rejaie,et al.  Mesh or Multiple-Tree: A Comparative Study of Live P2P Streaming Approaches , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[12]  David Hausheer,et al.  LiveShift: Mesh-pull live and time-shifted P2P video streaming , 2011, 2011 IEEE 36th Conference on Local Computer Networks.