Video Streaming over Cognitive Radio Networks

Abstract

The exponential increase in the demand for streaming video in wireless communication is obstructed by the problem of spectrum scarcity. In an effort to mitigate this problem, cognitive radio (CR) technology was proposed as a solution since it offers a great ad- vantage to unlicensed users, also known as secondary users (SUs), by allowing them to opportunistically access the licensed primary bands. However, it is more challenging to deliver video services over CR networks not only because of the intermittent availability of the PU channels but also due to the challenges stemming from wireless channels and the quality requirements of the videos. In this work, several frameworks are proposed to stream scalable video sequences from a base station to multiple SUs over CR networks. One approach is a moments matching-based approach that enabled us to quantify the total amount of data that can be provided by the available PU channels. Specifically, a closed-from approximation for the distribution of the total amount of data available for SU over all the available PU channels during any arbitrary interval of time was obtained. The correctness of the obtained closed-form approximation is verified using simulations and numerical investigations. Another approach is employing the CR network over long-term evolution (LTE) standard platform. The objective of this work is to guarantee continuous playback at the SUs end with acceptable perceptual quality. To achieve this objective, different resource allocation schemes are introduced to adaptively assign the available radio channels to SUs while taking into considerations the quality of their assigned channels as well as their buffer occupancies. In addition, a streaming algorithm is introduced to guarantee the delivery of scalable video frames, with base and enhancement layers, within the delay constraints with priority given to the base-layer frames to guarantee the continuity of video playback. Furthermore, adaptive modulation is used based on the channel state information (CSI) as fed-back by SUs. The performance of the proposed schemes is evaluated through extensive evaluation and Monte-Carlo simulations in Matlab.