In this study, we study the dynamics of network adaptive video streaming and propose novel algorithms for rate distortion control in video streaming. While doing so, we maintain inter-protocol fairness with TCP (Transmission Control Protocol) that is the dominant transport protocol in the current Internet. The proposed algorithms are retransmission-based and necessitate the use of playback buffers in order to tolerate the extra latency introduced by retransmissions. In the first part, we propose a practical network-adaptive streaming scheme based on TCP transport and the idea of Selective Frame Discarding (SFD) that makes use of two-layer temporally scalable video. The efficacy of the SFD scheme is validated for playout buffer times in the order of seconds and therefore makes it suitable more for delay tolerant streaming applications. In the second part of the thesis, we propose an application layer rate-distortion control algorithm which provides Optimal Scheduling and Rate Control (OSRC) policies in the average reward sense in order to achieve efficient streaming of video. The Optimal Scheduling (OS) we propose maximizes the probability of successfully on time delivery according to a prespecified set of rate constraints, and different channel conditions by using Markov Decision Process (MDP) models. On the other hand optimal rate control (RC) is achieved by calculating the optimal rate constraint which minimizes the average distortion of a video streaming session by making use of the video distortion model derived for lossy channels and achievable success probabilities provided by the set of optimal schedules. For numerical examples, we focus on an equation-based TCP friendly rate control (TFRC) protocol where transport layer retransmissions are disabled and Fine Granular Scalable (FGS) coded video is used for improved rate adaptation capabilities but with an additional rate distortion penalty. The efficacy of the proposed OSRC algorithm is demonstrated by means of both analytical results and ns-2 simulations.

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