A Novel Content Aware Channel Allocation Scheme for Video Applications over CRN



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.


Cognitive 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. 1.
    Spectrum Policy Task Force Report. (2002). Federal Communications Commission ET Docket 02 (Vol. 155).Google Scholar
  2. 2.
    Nguyen, V. T., Villain, F., & Guillou, Y. L. (2012). Cognitive radio RF: Overview and challenges. VLSI Design, 3, 1–13.CrossRefGoogle Scholar
  3. 3.
    Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23(2), 201–220.CrossRefGoogle Scholar
  4. 4.
    Amraoui, A., Benmammar, B., Krief, F., & Bendimerad, F. (2012). Intelligent wireless communication system using cognitive radio. International Journal of Distributed and Parallel Systems (IJDPS), 3(2), 91–104.CrossRefGoogle Scholar
  5. 5.
    Cisco. Visual Networking Index (VNI). http://www.cisco.com/. Accessed February 2014.
  6. 6.
    Lindeberg, M., Kristiansen, S., Plagemann, T., & Goebel, V. (2011). Challenges and techniques for video streaming over mobile ad hoc networks. Multimedia Systems, 17(1), 51–82.CrossRefGoogle Scholar
  7. 7.
    Hassan, M., & Krunz, M. (2007). Video streaming over wireless packet networks: An occupancy-based rate adaptation perspective. IEEE Transactions on Circuits and Systems for Video Technology, 17(8), 1017–1027.CrossRefGoogle Scholar
  8. 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
  9. 9.
    Kalman, M., Steinbach, E., & Girod, B. (2004). Adaptive media playout for low-delay video streaming over error-prone channels. IEEE Transactions on Circuits and Systems for Video Technology, 14, 841–851.CrossRefGoogle Scholar
  10. 10.
    Wang, Y., & Zhu, Q. F. (1998). Error control and concealment for video communication: A review. Proceedings of the IEEE, 86(9), 974–997.CrossRefGoogle Scholar
  11. 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
  12. 12.
    Kushwaha, H., Xing, Y., Chandramouli, R., & Heffes, H. (2008). Reliable multimedia transmission over cognitive radio networks using fountain codes. Proceedings of the IEEE, 96(1), 155–165.CrossRefGoogle Scholar
  13. 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
  14. 14.
    Hu, D., & Mao, S. (2010). Streaming scalable videos over multi-hop cognitive radio networks. IEEE Transactions on Wireless Communications, 9(11), 3501–3511.CrossRefGoogle Scholar
  15. 15.
    Hu, D., & Mao, S. (2012). On cooperative relay networks with video applications. arXiv preprint: arXiv:1209.2086.
  16. 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
  17. 17.
    Xu, Y., Hu, D., & Mao, S. (2014). Relay-assisted multiuser video streaming in cognitive radio networks. IEEE Transactions on Circuits and Systems for Video Technology, 24(10), 1758–1770.CrossRefGoogle Scholar
  18. 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
  19. 19.
    Hassan, M. S., Abusara, A., El Din, M. S., & Ismail, M. H. (2016). On efficient channel modeling for video transmission over cognitive radio networks. Wireless Personal Communications.  https://doi.org/10.1007/s11277-016-3504-5.Google Scholar
  20. 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
  21. 21.
    Zeng, Y., Liang, Y. C., Hoang, A. T., & Zhang, R. (2010). A review on spectrum sensing for cognitive radio: Challenges and solutions. EURASIP Journal on Advances in Signal Processing, 2010(1), 1–15.CrossRefGoogle Scholar
  22. 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
  23. 23.
    Kim, H., & Shin, K. G. (2008). Efficient discovery of spectrum opportunities with MAC-layer sensing in cognitive radio networks. IEEE Transactions on Mobile Computing, 7(5), 533–545.CrossRefGoogle Scholar
  24. 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. 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

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations