Skip to main content
SpringerLink
Log in

Reliable rate-adaptive video transmission over cognitive cellular networks using multiple description scalable coding

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

The promise of high-bandwidth and low-latency wireless applications has placed unprecedented stress on the limited spectrum for future 5G networks. Thus, dynamic spectrum sharing has emerged as a promising solution to increase the capacity and the coverage of future cellular networks. In this paper, we tackle the issue of video delivery over cognitive cellular networks using a combination of the legacy licensed bands as well as the spectrum holes left unused in TV bands. To actively react to fluctuating bandwidth, the original video undergoes a multiple description scalable coding (MDSC), whereby the video sequence is fragmented into odd and even sub-streams, then the decomposed video is hierarchically encoded using H.264/SVC to produce two independent descriptions that refine each other. As long as the available licensed radio resources are not sufficient to deliver all the scalable video layers, the cognitive base station exploits the available portion of TV spectrum using a hybrid interweave and underlay approach to extend the capacity of the legacy infrastructure. Numerical simulations show that the proposed transmission scheme provides enough capacity, up to \(1.62~\mathrm {Mbps}\) increase, to handle the rising demands in terms of video quality in a timely manner without disrupting the primary link and without connection release. Moreover, the proposed MDSC framework offers an increment of 20% (24%, respectively) in the PSNR (MS-SSIM, respectively) of the received video.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Cisco. (2017). Cisco visual networking index: Global mobile data traffic forecast update (2016–2021). White paper.

  2. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.

    Article  Google Scholar 

  3. Goldsmith, A., & Maric, I. (2012). Capacity of cognitive radio networks. In E. Biglieri, A. J. Goldsmith, L. J. Greenstein, N. Mandayam, & H. V. Poor (Eds.), Principles of cognitive radio, (Vol. 11, Chap. 2, pp. 41–101). Cambridge University Press.

  4. Ericsson ConsumerLab. (2015). How indoors influence choice of devices in Europe. https://www.ericsson.com/assets/local/news/2015/5/ericsson-consumerlab-the-indoor-influence-europe.pdf/. Accessed 11 February 2018.

  5. Sherman, M., Mody, A. N., Martinez, R., Rodriguez, C., & Reddy, R. (2008). IEEE standards supporting cognitive radio and networks, dynamic spectrum access, and coexistence. IEEE Communications Magazine, 46(7), 72–79.

    Article  Google Scholar 

  6. Sachs, J., Maric, I., & Goldsmith, A. (2010). Cognitive cellular systems within the TV spectrum. In IEEE symposium on new frontiers in dynamic spectrum (pp. 1–12). IEEE.

  7. Hong, X., Wang, J., Wang, C.-X., & Shi, J. (2014). Cognitive radio in 5G: A perspective on energy-spectral efficiency trade-off. IEEE Communications Magazine, 52(7), 46–53.

    Article  Google Scholar 

  8. Schwarz, H., Marpe, D., & Wiegand, T. (2007). Overview of the scalable video coding extension of the H. 264/AVC standard. IEEE Transactions on Circuits and Systems for Video Technology, 17(9), 1103–1120.

    Article  Google Scholar 

  9. Ligang, Z. W., Wang, Z., Lu, L., & Bovik, A. C. (2002). Video quality assessment using structural distortion measurement. In Proc. IEEE Int. Conf. Image Proc. Citeseer.

  10. Kushwaha, H., Xing, Y., Chandramouli, R., & Subbalakshmi, K. P. (2007). Erasure tolerant coding for cognitive radios. In Q. H. Mahmoud (Ed.), Cognitive networks: Towards self-aware networks (Chap. 13, pp. 315–331). John Wiley & Sons.

  11. Li, H. (2010). Multiple description source coding for cognitive radio systems. In Fifth international conference on cognitive radio oriented wireless networks and communications (CROWNCOM), pp 1–5. IEEE.

  12. Chaoub, A., Elhaj, E. I., & El Abbadi, J. (2011). Multimedia traffic transmission over cognitive radio networks using multiple description coding. In International conference on advances in computing and communications, pp. 529–543. Springer.

  13. Chaoub, A., & Ibn-Elhaj, E. (2012). Multiple description coding for cognitive radio networks under secondary collision errors. In 16th IEEE mediterranean electrotechnical conference (MELECON), pp. 27–30. IEEE.

  14. Chaoub, A., & Ibn-Elhaj, E. (2013). Cross layer design for equal and unequal loss protection frameworks in cognitive radio networks. Computers & Electrical Engineering, 39(2), 571–581.

    Article  Google Scholar 

  15. Albanese, A., Blomer, J., Edmonds, J., Luby, M., & Sudan, M. (1996). Priority encoding transmission. IEEE Transactions on Information Theory, 42(6), 1737–1744.

    Article  Google Scholar 

  16. Javadi, G., Hajshirmohammadi, A., & Liang, J. (2017). Power and sub-channel optimization of JPEG 2000 image transmission over OFDM-based cognitive radio networks. Signal Processing: Image Communication, 58, 157–164.

    Google Scholar 

  17. Naeem, A., Rehmani, M. H., Saleem, Y., Rashid, I., & Crespi, N. (2017). Network coding in cognitive radio networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 19(3), 1945–1973.

    Article  Google Scholar 

  18. Farrokhi, H., & Pourmohammadi, I. (2017). Adaptive rateless coding technique for data dissemination in multichannel multiuser cognitive radio networks. Wireless Personal Communications, 96(2), 2463–2484.

    Article  Google Scholar 

  19. Amjad, M., Rehmani, M. H., & Mao, S. (2018). Wireless multimedia cognitive radio networks: A comprehensive survey. IEEE Communications Surveys Tutorials, PP(99), 1–1.

    Google Scholar 

  20. Chaoub, A., & Ibn-Elhaj, E. (2014). Reliable multicast video transmission over cognitive radio networks: Equal or unequal loss protection? In F. Hu & S. Kumar (Eds.), Multimedia over cognitive radio networks: Algorithms, protocols, and experiments, chapter 9 (pp. 221–242). Boca Raton: CRC Press.

    Google Scholar 

  21. He, Z., & Mao, S. (2014). Adaptive multiple description coding and transmission of uncompressed video over 60ghz networks. ACM SIGMOBILE Mobile Computing and Communications Review, 18(1), 14–24.

    Article  Google Scholar 

  22. Karim, H. A., Mohamad, H., Ramli, N., & Sali, A. (2015). Multiple description video coding for underlay cognitive radio network. In International conference on cognitive radio oriented wireless networks (CROWNCOM), pp. 643–652. Springer.

  23. Google. (2017). Spectrum database. https://www.google.com/get/spectrumdatabase/. Accessed 11 February 2018.

  24. Digham, F. F., Alouini, M.-S., & Simon, M. K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications, 55(1), 21–24.

    Article  Google Scholar 

  25. Van der Schaar, M., & Turaga, D. S. (2003). Multiple description scalable coding using wavelet-based motion compensated temporal filtering. In International conference on image processing (ICIP), vol. 3, pp. III–489. IEEE.

  26. Apostolopoulos, J. G. (2000). Error-resilient video compression through the use of multiple states. In International conference on image processing (ICIP), vol. 3, pp. 352–355. IEEE.

  27. Joint Scalable Video Model. (2012). JSVM 9.19.15 software package. CVS server for JSVM reference softwares.

  28. Multimedia Signal Processing Group (MMSPG). (2013). Video quality measurement tool (vqmt). http://mmspg.epfl.ch/vqmt. Accessed 13 June 2018.

  29. H.264/SVC. (2015). Test sequences. ftp://ftp.tnt.uni-hannover.de/pub/svc/testsequences. Accessed 07 June 2018.

Download references

Author information

Authors and Affiliations

  1. STRS Laboratory, MUSICS Team, National Institute of Posts and Telecommunications (INPT), Rabat, Morocco

    Abdelaali Chaoub, Fatim Zahra Ennaoui & Elhassane Ibn-Elhaj

Authors
  1. Abdelaali Chaoub
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatim Zahra Ennaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Elhassane Ibn-Elhaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdelaali Chaoub.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaoub, A., Ennaoui, F.Z. & Ibn-Elhaj, E. Reliable rate-adaptive video transmission over cognitive cellular networks using multiple description scalable coding. Telecommun Syst 71, 321–338 (2019). https://doi.org/10.1007/s11235-018-0498-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-018-0498-1

Keywords

Search

Navigation