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On Efficient Channel Modeling for Video Transmission over Cognitive Radio Networks

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Abstract

This paper investigates the problem of video transmission over cognitive radio networks with the objective of maintaining continuous video playback while gracefully degrading the quality of the reconstructed video sequences, if needed. We focus on modeling the channel availability to secondary users, which is a major limiting factor on the continuity of the streaming process. A Markov chain model for the channels availability in an M-channels system is developed. This model is used to estimate the likelihood of transmission interruptions a secondary user might experience due to the loss of a channel to a primary user. We also propose a joint adaptive mechanism where a simple source rate control scheme is integrated with an adaptive playback approach to reduce the impact of channels relocation/unavailability on the streaming process of active secondary users. Simulations and numerical investigations demonstrate the correctness of the proposed channel model. Simulation results also indicate that instants of playback buffer starvation at the secondary user ends could be avoided only when the hybrid approach is employed

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References

  1. “New America Foundation @ONLINE.” https://www.newamerica.org/publications/policy/end_spectrum_scarcity. Accessed 6 Mar (2015).

  2. Mitola, J. (2000). Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio. PhD thesis, Royal Institute of Technology (KTH).

  3. Haykin, S. (2005). Cognitive radio: Brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23, 201–220.

    Article  Google Scholar 

  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, 91–104.

    Article  Google Scholar 

  5. Nguyen, T., Villain, V. F., & Guillou, Y. L. (2012). Cognitive radio RF: Overview and challenges. VLSI Design, 3, 1–13.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  7. Hassan, M., Atzori, L., & Krunz, M. (2004). Video transport over wireless channels: A cycle-based approach for rate control, Proceedings of the 12th Annual ACM International Conference on Multimedia, MULTIMEDIA ’04 (pp. 916–923). New York, NY, USA: ACM.

  8. 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, 1017–1027.

    Article  Google Scholar 

  9. chi Lee, Y., Kim, J., Altunbasak, Y., & Mersereau, R.M. (2003). Layered coded vs. multiple description coded video over error-prone networks, Signal Processing: Image Communication (pp. 337–356).

  10. Wang, Y., & Zhu, Q.-F. (1998). Error control and concealment for video communication: A review. Proceedings of the IEEE, 86, 974–997.

    Article  Google Scholar 

  11. Hassan, M., & Krunz, M. (2005). A playback-adaptive approach for video streaming over wireless networks, Global Telecommunications Conference, 2005. GLOBECOM ’05. IEEE, vol. 6, (pp. 5–3691).

  12. 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.

    Article  Google Scholar 

  13. Hu, D., & Mao, S. (2012). On cooperative relay networks with video applications. arXiv preprint. http://arxiv.org/pdf/1209.2086.pdf.

  14. Li, S., Luan, T.H., & Shen, X. (Dec 2010). Channel allocation for smooth video delivery over cognitive radio networks, Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, (pp. 1–5).

  15. Xu, H., & Li, B. (2013). Resource allocation with flexible channel cooperation in cognitive radio networks. IEEE Transactions on Mobile Computing, 12, 957–970.

    Article  Google Scholar 

  16. 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, 1758–1770.

    Article  Google Scholar 

  17. Hu, D., & Mao, S. (2010). Streaming scalable videos over multi-hop cognitive radio networks. IEEE Transactions on Wireless Communications, 9, 3501–3511.

    Article  Google Scholar 

  18. Hu, D., & Mao, S. (2012). On medium grain scalable video streaming over femtocell cognitive radio networks. IEEE Journal on Selected Areas in Communications, 30, 641–651.

    Article  Google Scholar 

  19. Marpe, D., Wiegand, T., & Sullivan, G. J. (2006). The H.264/MPEG4 advanced video coding standard and its applications. IEEE Communications Magazine, 44, 134–143.

    Article  Google Scholar 

  20. Hassan, M., & Landolsi, T. (2009). A retransmission-based scheme for video streaming over wireless channels. Wireless Communications and Mobile Computing, 10, 511–521.

    Google Scholar 

  21. Seeling, P., & Reisslein, M. (2012). Video transport evaluation with H.264 video traces, IEEE Communications Surveys Tutorials, vol. 14, (pp. 1142–1165), Fourth.

  22. Gupta, R., Pulipaka, A., Seeling, P., Karam, L. J., & Reisslein, M. (2012). H.264 coarse grain scalable ( CGS) and medium grain scalable ( MGS) encoded video: A trace based traffic and quality evaluation. IEEE Transactions on Broadcasting, 58, 428–439.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, American University of Sharjah, PO Box 26666, Sharjah, UAE

    Mohamed S. Hassan, Ayah Abusara, Menatalla Shehab El Din & Mahmoud H. Ismail

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  1. Mohamed S. Hassan
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  2. Ayah Abusara
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  3. Menatalla Shehab El Din
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  4. Mahmoud H. Ismail
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Correspondence to Mohamed S. Hassan.

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Hassan, M.S., Abusara, A., Din, M.S.E. et al. On Efficient Channel Modeling for Video Transmission over Cognitive Radio Networks. Wireless Pers Commun 91, 919–932 (2016). https://doi.org/10.1007/s11277-016-3504-5

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  • DOI: https://doi.org/10.1007/s11277-016-3504-5

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