Journal of Network and Systems Management

, Volume 24, Issue 3, pp 516–533 | Cite as

Traffic Prediction for Reliable and Resilient Video Communications Over Multi-Location WMNs

  • Bo RongEmail author
  • Songlin Sun
  • Michel Kadoch


This paper studies reliable and resilient deployment of video over IP across a multi-location organization, where a number of wireless mesh network (WMN) clouds are connected by a virtual private network (VPN). Particularly, we propose a scheme of enhanced SIP proxy server which can support an accountable network. In our proposed solution, the enhanced SIP proxy server consists of three modules, namely traffic load prediction, VPN bandwidth negotiation, and call admission control (CAC) in order to provide trustable service. We identify traffic load prediction as the key component among the three modules, and we further develop a linear predictor of variable sampling rate-normalized least mean square (VSR-NLMS) to estimate the traffic patterns. VSR-NLMS predictor employs adjustable sampling rate to achieve improved efficiency and performance. Numerical results show that our proposed scheme can automatically choose suitable sampling rate to track and predict the traffic load curve with acceptable accuracy and reasonable computational complexity.


Video over IP SIP proxy server Wireless mesh network Virtual private network 


  1. 1.
    Wang, X., Kwon, T.T., Choi, Y., Wang, H., Liu, J.: Cloud-assisted adaptive video streaming and social-aware video prefetching for mobile users. IEEE Wirel. Commun. 20(3), 72–79 (2013)CrossRefGoogle Scholar
  2. 2.
    Tseng, Y., Wu, E., Chen, G.: Scene-change cware dynamic bandwidth allocation for real-time VBR video transmission over IEEE 802.15.3 wireless home networks. IEEE Trans. Multimed. 9(3), 642–654 (2007)CrossRefGoogle Scholar
  3. 3.
    Zhou, L., Hu, R.Q., Qian, Yi: Energy-spectrum efficiency tradeoff for video streaming over mobile ad-hoc networks. IEEE J. Sel. Areas Commun. 31(5), 981–991 (2013)CrossRefGoogle Scholar
  4. 4.
    Hu, R.Q., Qian, Y.: An energy efficient and spectrum efficient wireless heterogeneous network framework for 5G systems. IEEE Commun. 52(5), 94–101 (2014)CrossRefGoogle Scholar
  5. 5.
    Li, Q., Hu, R.Q., Qian, Y., Wu, G.: Cooperative communications for wireless networks: techniques and applications in LTE-advanced systems. IEEE Wirel. Commun. 19(2), 22–29 (2012)Google Scholar
  6. 6.
    Yan, Y., Qian, Y., Sharif, H., Tipper, D.: A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun. Surv. Tutor. 15(1), 5–20 (2013)CrossRefGoogle Scholar
  7. 7.
    Hu, G., Huang, A., Chang, T., Cheng, X., Wu, H., Xie, L., Xu, A., Chen, Z.: A sensor-based seamless handover solution for express train access networks (ETANs). IEEE Commun. Lett. 16(4), 470–472 (2012)CrossRefGoogle Scholar
  8. 8.
    Zhang, Y., Xu, L., Xiang, Y., Huang, X.: Matrix-based pairwise key establishment in wireless mesh networks using deployment knowledge. In: 2013 IEEE International Conference on Communications (ICC), pp. 1604–1608, 9–13 June (2013)Google Scholar
  9. 9.
    Sui, Y., Vihriala, J., Papadogiannis, A., Sternad, M., Yang, W., Svensson, T.: Moving cells: a promising solution to boost performance for vehicular users. IEEE Commun. Mag. 51(6), 62–68 (2013)CrossRefGoogle Scholar
  10. 10.
    Lu, K., Qian, Y., Guizani, M., Chen, H.H.: A framework for a distributed key management scheme in heterogeneous wireless sensor networks. IEEE Trans. Wirel. Commun. 7(2), 639–647 (2008)CrossRefGoogle Scholar
  11. 11.
    Zhang, D., Ionescu, D.: Measurement and control of packet loss probability for MPLS VPN services. IEEE Trans. Instrum. Meas. 55(5), 1587–1598 (2006)CrossRefGoogle Scholar
  12. 12.
    Martinez-Yelmo, I., Larrabeiti, D., Soto, I., Pacyna, P.: Multicast traffic aggregation in MPLS-based VPN networks. IEEE Commun. Mag. 45(10), 78–85 (2007)CrossRefGoogle Scholar
  13. 13.
    Achour, A., Haddadou, K., Kervella, B., Pujolle, G.: A SIP-SHIM6-based solution providing interdomain service continuity in IMS-based networks. IEEE Commun. Mag. 50(7), 109–119 (2012)CrossRefGoogle Scholar
  14. 14.
    Rong, B., Qian, Y., Lu, K., Hu, R.Q., Kadoch, M.: Mobile-agent-based handoff in wireless mesh networks: architecture and call admission control, vehicular technology. IEEE Trans. 58(8), 4565,4575 (2009)Google Scholar
  15. 15.
    Rong, B., Qian, Y., Lu, K., Hu, R.Q., Kadoch, M.: Multipath routing over wireless mesh networks for multiple description video transmission. IEEE J. Sel. Areas Commun. 28(3), 321–331 (2010)CrossRefGoogle Scholar
  16. 16.
    Sun, S., Ju, Y., Yamao, Y.: Overlay cognitive radio OFDM system for 4G cellular networks. Wireless Commun. IEEE 20(2), 68,73 (2013)CrossRefGoogle Scholar
  17. 17.
    Yan, Y., Qian, Y., Sharif, H., Tipper, D.: A survey on cyber security for smart grid communications. IEEE Commun. Surv. Tutor. 14(4), 998C1010 (2012)CrossRefGoogle Scholar
  18. 18.
    Lu, K., Qian, Y., Chen, H.H.: A secure and service-oriented network control framework for WiMAX networks. IEEE Commun. 45(5), 124–130 (2007)CrossRefGoogle Scholar
  19. 19.
    Liu, T., Liao, W.: Interference-aware QoS routing for multi-rate multi-radio multi-channel IEEE 802.11 wireless mesh networks. IEEE Trans. Wirel. Commun. 8(1), 166–175 (2009)CrossRefGoogle Scholar
  20. 20.
    Shen, Q., Fang, X., Li, P., Fang, Y.: Admission control based on available bandwidth estimation for wireless mesh networks. IEEE Trans. Veh. Technol. 58(5), 2519–2528 (2009)CrossRefGoogle Scholar
  21. 21.
    Rong, B., Qian, Y., Lu, K., Chen, H.-H., Guizani, M.: Call admission control optimization in WiMAX networks. IEEE Trans. Veh. Technol. 57(4), 2509–2522 (2008)CrossRefGoogle Scholar
  22. 22.
    Yoo, S.J.: Efficient traffic prediction scheme for real-time VBR MPEG video transmission over high-speed networks. IEEE Trans. Broadcast. 48(1), 10–18 (2002)CrossRefGoogle Scholar
  23. 23.
    Haykin, S.: Adaptive Filter Theory. Prentice Hall, New Jersey (1991)zbMATHGoogle Scholar
  24. 24.
    Kwong, R.H., Johnston, E.W.: A variable step size LMS algorithm. IEEE Trans. Signal Process. 40(7), 1633–1642 (1992)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Communications Research Center CanadaOttawaCanada
  2. 2.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.Department of Electrical Engineering, Ecole de Technologie SuperieureUniversite du QuebecMontrealCanada

Personalised recommendations