Fundamental tradeoffs in distributed algorithms for rate adaptive multimedia streams

Rate adaptive multimedia streams adjust the encoding rate dynamically (with corresponding changes in media content resolution) in response to changing levels of congestion along the route. 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. 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. We illustrate these tradeoffs through extensive ns-2 simulations on two distinct topologies – (i) a single bottleneck like and (ii) a linear network.