Video Streaming over Cognitive Radio Networks

In recent years, there has been a tremendous growth in multimedia applications over the wireless Internet. The significant bandwidth requirement for multimedia services has increased the demand for radio spectrum. The scarcity of radio spectrum has challenged the conventional fixed spectrum assignment policy. As a result, cognitive radio emerged as a new paradigm to address the spectrum underutilization problem by enabling users to opportunistically access unused spectrum bands. In this thesis, we propose a framework for video transmission over cognitive radio networks. Our objective is to determine the optimal streaming policy in order to maximize the overall perceived video quality while keeping quality fluctuation at minimum. In our framework, we introduce a channel usage model based on a two-state Markov model and estimate the future busy and idle durations of the spectrum based on past observations. On the basis of this scheme, we formulate the streaming optimization problem under the constraint of the available bandwidth budget so that the optimal number of enhancement layer bits are assigned to each frame. We extend this algorithm for three different optimization levels: frame, GOP and scene. We evaluate our algorithm through extensive trace-driven simulation, and show that it improves the perceived video quality and increases bandwidth utilization