In terms of scalability, cost and ease of deployment, the Peer-to-Peer (P2P) approach has emerged as a promising solution for video streaming applications. Its architecture enables end-hosts, called peers, to relay the video stream to each other. P2P systems are in fact networks of users who control peers. Thus, user behavior is crucial to the performance of these systems because it directly impacts the streaming flow. To understand user behavior, several measurement studies have been carried out over different video streaming systems. Each measurement analyzes a particular system focusing on specific metrics and presents insights. However, a single study based on a particular system and specific metrics is not sufficient to provide a complete model of user behavior considering all of its components and the impact of external factors on them. In this paper, we propose a comparison and a synthesis of these measurements. First of all, we review video streaming architectures, followed by a survey on the user behavior measurements in these architectures. Then, we gather insights revealed in these measurements and compare them for consensual and contrasting points. Finally, we extract components of user behavior, their external impacting factors and relationships among them. We also point out those aspects of user behavior which require further investigations.
Abstract. Live video streaming over a Peer-to-Peer (P2P) architecture is promising due to its scalability and ease of deployment. Nevertheless, P2P-based video streaming systems still face some challenges regarding their performance. These systems are in fact overlays of users who control peers. As peers depend upon each other for receiving the video stream, the user behavior has an impact over the performance of the system. We collect the user behavior studies over live video streaming systems and identify the impact of different user activities on the performance. Based on this information, we propose a Bayesian network that models a generic user behavior initially and then adapts itself to individuals through learning from observations. We validate our model through simulations.
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