A Novel Content Aware Channel Allocation Scheme for Video Applications over CRN
- 3 Downloads
Cognitive radio (CR) has emerged as an effective solution to spectrum scarcity problem which efficiently utilizes the unused spectrum of licensed primary user (PU). Video applications, as a bandwidth intensive and delay-sensitive application, will surely get benefitted from CR technology due to its ability to provide additional bandwidth to end users. In this article we investigate the challenges of quality of experience (QoE) driven video applications over CR networks due to the random behavior of PUs, dynamic characteristic of the primary channels, packet error rate etc. Generally, all video applications could be categorized into three groups like slight motion, gentle walking and rapid motion (RM) and each group has its own quality of service (QoS) requirements. The aim of this paper is to minimize QoE degradation by estimating the quality of the available channels based on our proposed Channel Quality Index metric and then allocating the channels depending on the QoS requirements of a particular video application. Extensive analysis validates that there is a performance enhancement of different video applications, especially RM type (nearly 66%) which is considered as most critical among all.
KeywordsCognitive radio Quality of service Quality of experience MOS Channel allocation Channel Quality Index
The authors deeply acknowledge the support from Visvesvaraya PhD Scheme, (DeitY), Govt. of India.
- 1.Spectrum Policy Task Force Report. (2002). Federal Communications Commission ET Docket 02 (Vol. 155).Google Scholar
- 5.Cisco. Visual Networking Index (VNI). http://www.cisco.com/. Accessed February 2014.
- 8.Hassan, M., & Krunz, M. (2005). A playback-adaptive approach for video streaming over wireless networks. In Global telecommunications conference, GLOBECOM’05 (Vol. 6, pp. 3687–3691). IEEE.Google Scholar
- 11.Lee, Y. C., Kim, J., Altunbasak, Y., & Mersereau, R. M. (2003). Layered coded vs. multiple description coded video over error-prone networks. Signal Processing: Image Communication, 18(5), 337–356.Google Scholar
- 13.Ali, S., & Yu, F. R. (2009, April). Cross-layer QoS provisioning for multimedia transmissions in cognitive radio networks. In Wireless communications and networking conference, 2009. WCNC 2009 (pp. 1–5). IEEE.Google Scholar
- 15.Hu, D., & Mao, S. (2012). On cooperative relay networks with video applications. arXiv preprint: arXiv:1209.2086.
- 16.Li, S., Luan, T. H., & Shen, X. (2010, December). Channel allocation for smooth video delivery over cognitive radio networks. In 2010 IEEE global telecommunications conference (GLOBECOM 2010) (pp. 1–5). IEEE.Google Scholar
- 18.Bhattacharya, A., Ghosh, R., Sinha, K., & Sinha, B. P. (2011, January). Multimedia communication in cognitive radio networks based on sample division multiplexing. In 2011 Third international conference on communication systems and networks (COMSNETS) (pp. 1–8). IEEE.Google Scholar
- 20.Khan, A., Sun, L., & Ifeachor, E. (2009, June). Content clustering based video quality prediction model for MPEG4 video streaming over wireless networks. In IEEE international conference on communications, 2009. ICC’09 (pp. 1–5). IEEE.Google Scholar
- 22.Vujičić, B., Cackov, N., Vujičić, S., & Trajković, L. (2005). Modeling and characterization of traffic in public safety wireless networks. In Proceedings of SPECTS.Google Scholar
- 24.He, Z., Mao, S., & Kompella, S. (2014, December). QoE driven video streaming in cognitive radio networks: The case of single channel access. In 2014 IEEE global communications conference (GLOBECOM) (pp. 1388–1393). IEEE.Google Scholar
- 25.Ciftci, S., & Torlak, M. (2008, November). A comparison of energy detectability models for spectrum sensing. In Global telecommunications conference, 2008. IEEE GLOBECOM 2008 (pp. 1–5). IEEE.Google Scholar