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Popular peer to peer streaming media systems such as PPLive and UUSee rely onperiodic buffer-map exchange between peers for proper operation. The buffer-mapexchange contains redundant information which causes non-negligible overhead.In this paper we present a theoretical framework to study how the overhead canbe lowered. Differentiating from the traditional data compression approach, wedo not treat each buffer-map as an isolated data block, but consider thecorrelations between the sequentially exchanged buffer-maps. Under thisframework, two buffer-map compression schemes are proposed and the correctnessof the schemes is proved mathematically. Moreover, we derive the theoreticallimit of compression gain based on probability theory and information theory.Based on the system parameters of UUSee (a popular P2P streaming platform), oursimulations show that the buffer-map sizes are reduced by 86% and 90% (from 456bits down to only 66 bits and 46 bits) respectively after applying our schemes.Furthermore, by combining with the traditional compression methods (onindividual blocks), the sizes are decreased by 91% and 95% (to 42 bits and 24bits) respectively. Our study provides a guideline for developing practicalcompression algorithms.

Buffer Map Message Compression Based on Relevant Window in P2P Streaming Media System