Fundamental Tradeoffs in Distributed Algorithms for Rate Adaptive Multimedia Streams

Rate adaptive multimedia streams are streaming media connections where the encoding rate is adjusted dynamically (with corresponding changes in media content resolution) in response to changing levels of congestion along the connection. The field of optimization based congestion control has yielded sophisticated distributed algorithms for resource allocation among competing elastic streams. In this work we study the fundamental tradeoffs for a class of optimization based distributed algorithms for rate adaptive streams, building on our earlier work. We focus on three tradeoffs: i) the tradeoff between maximizing client average quality of service (QoS) and client fairness, ii) the tradeoff between granularity of control (both temporal and spatial) and QoS, and iii) the tradeoff between maximizing the received volume and minimizing the fluctuations in received rate. These tradeoffs are illustrated through extensive simulation results using ns-2.