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Mobile Information Systems
Volume 2015 (2015), Article ID 934715, 7 pages
Research Article

Channel Allocation Based on Content Characteristics for Video Transmission in Time-Domain-Based Multichannel Cognitive Radio Networks

Electronics and Radio Engineering, Yongin-si 446-701, Republic of Korea

Received 27 July 2015; Revised 18 August 2015; Accepted 19 August 2015

Academic Editor: Yuh-Shyan Chen

Copyright © 2015 Md. Jalil Piran et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a method for channel allocation based on video content requirements and the quality of the available channels in cognitive radio networks (CRNs). Our objective is to save network bandwidth and achieve high-quality video delivery. In this method, the content is divided into clusters based on scene complexity and PSNR. To allocate channel to the clusters over multichannel CRNs, we first need to identify the licensee’s activity and then maximize the opportunistic usage accordingly. Therefore, we classify short and long time transmission opportunities based on the licensee’s activities using a Bayesian nonparametric inference model. Furthermore, to prevent transmission interruption, we consider the underlay mode for transmission of the clusters with a lower bitrate. Next, we map the available spectrum opportunities to the content clusters according to both the quality of the channels and the requirements of the clusters. Then, a distortion optimization model is constructed according to the network transmission mechanism. Finally, to maximize the average quality of the delivered video, an optimization problem is defined to determine the best bitrate for each cluster by maximizing the sum of the logarithms of the frame rates. Our extensive simulation results prove the superior performance of the proposed method in terms of spectrum efficiency and the quality of delivered video.