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Internet-Based Statistical Congestion Control for Non-Adaptable Multimedia Streams

This thesis presents a TCP-friendly congestion control scheme for non-adaptable flows. The main characteristic of these flows is that their data rate is determined by the producing application and cannot be adapted to the current congestion situation of the network. Typical examples of nonadaptable flows are those produced by networked computer games or live audio and video transmissions where adaption of the quality is not possible (e.g., since it is already at the lowest possible level). We propose to perform congestion control for non-adaptable flows by suspending them at appropriate times so that the aggregation of multiple non-adaptable flows behaves in a TCP-friendly manner. The decision whether a flow is suspended is based on random experiments. In order to allocate probabilities for these experiments, the data rate of the non-adaptable flow is compared to the rate that a TCP flow would have under the same conditions. Because random experiments are used to probabilistically adapt the aggregate rate of multiple flows, this congestion control scheme is called Probabilistic Congestion Control or PCC. We present a detailed discussion of the PCC scheme and an evaluation based on extensive simulations with the network simulator ns-2.