Performance evaluation of TcpHas: TCP for HTTP adaptive streaming

  • Chiheb Ben Ameur
  • Emmanuel Mory
  • Bernard Cousin
  • Eugen Dedu
Regular Paper


HTTP adaptive streaming (HAS) is a widely used video streaming technology that suffers from a degradation of user’s Quality of Experience (QoE) and network’s Quality of Service (QoS) when many HAS players are sharing the same bottleneck link and competing for bandwidth. The two major factors of this degradation are: the large OFF period of HAS, which causes false bandwidth estimations, and the TCP congestion control, which is not suitable for HAS given that it does not consider the different video encoding bitrates of HAS. This paper proposes a HAS-based TCP congestion control, TcpHas, that minimizes the impact of the two aforementioned issues. It does this using traffic shaping on the server. Simulations indicate that TcpHas improves both QoE, mainly by reducing instability and convergence speed, and QoS, mainly by reducing queuing delay and packet drop rate.


HTTP adaptive streaming TCP congestion control Cross-layer optimization Traffic shaping Quality of Experience Quality of Service 


  1. 1.
    Abdallah, A., Meddour, D.E., Ahmed, T., Boutaba, R.: Cross layer optimization architecture for video streaming in WiMAX networks. In: 2010 IEEE Symposium on Computers and Communications (ISCC). IEEE, pp. 8–13 (2010)Google Scholar
  2. 2.
    Akhshabi, S., Anantakrishnan, L., Begen, A.C., Dovrolis, C.: What happens when HTTP adaptive streaming players compete for bandwidth? In: Proceedings of the 22nd international workshop on Network and Operating System Support for Digital Audio and Video. ACM, pp. 9–14 (2012)Google Scholar
  3. 3.
    Akhshabi, S., Anantakrishnan, L., Dovrolis, C., Begen, A.C.: Server-based traffic shaping for stabilizing oscillating adaptive streaming players. In: 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, pp. 19–24 (2013)Google Scholar
  4. 4.
    Allman, M., Paxson,V., Blanton, E. TCP congestion control. RFC 5681 (2009)Google Scholar
  5. 5.
    Ammar, D.: PPBP in ns-3. (2016)
  6. 6.
    Ammar, D., Begin, T., Guerin-Lassous, I.: A new tool for generating realistic internet traffic in ns-3. In: 4th International ICST Conference on Simulation Tools and Techniques, pp. 81–83 (2011)Google Scholar
  7. 7.
    Ben Ameur, C., Mory, E., Cousin, B.: Shaping HTTP adaptive streams using receive window tuning method in home gateway. In: IEEE International Conference on Performance Computing and Communications (IPCCC), pp. 1–2 (2014)Google Scholar
  8. 8.
    Ben Ameur, C., Mory, E., Cousin, B.: Evaluation of gateway-based shaping methods for HTTP adaptive streaming. In: Quality of Experience-based Management for Future Internet Applications and Services (QoE-FI) Workshop. IEEE International Conference on Communications (ICC), London, pp. 1–6 (2015)Google Scholar
  9. 9.
    Ben Ameur, C., Mory, E., Cousin, B.: Combining traffic shaping methods with congestion control variants for HTTP adaptive streaming. Multimed. Syst., 1–18 (2016)Google Scholar
  10. 10.
    Ben Ameur, C., Mory, E., Cousin, B., Dedu, E.: TcpHas: TCP for HTTP adaptive streaming. IEEE International Conference on Communications (ICC). IEEE, Paris, pp. 1–7 (2017)Google Scholar
  11. 11.
    Capone, A., Martignon, F., Palazzo, S.: Bandwidth estimates in the TCP congestion control scheme. In: Thyrrhenian International Workshop on Digital Communications: Evolutionary Trends of the Internet (IWDC). Springer, New York, pp. 614–626 (2001)Google Scholar
  12. 12.
    Capone, A., Fratta, L., Martignon, F.: Bandwidth estimation schemes for TCP over wireless networks. IEEE Trans. Mobile Comput. 3(2), 129–143 (2004)CrossRefGoogle Scholar
  13. 13.
    Cheng, Y.: HTTP traffic generator. (2016)
  14. 14.
    Cheng, Y., Çetinkaya, E.K., Sterbenz, J.P.: Transactional traffic generator implementation in ns-3. In: 6th International ICST Conference on Simulation Tools and Techniques, pp. 182–189 (2013)Google Scholar
  15. 15.
  16. 16.
    Dedu, E., Ramadan, W., Bourgeois, J.: A taxonomy of the parameters used by decision methods for adaptive video transmission. Multimed. Tools Appl. 74(9), 2963–2989 (2015)CrossRefGoogle Scholar
  17. 17.
    Floyd, S., Handley, M., Padhye, J., Widmer, J.: TCP Friendly Rate Control (TFRC): Protocol specification. RFC 5348 (2008)Google Scholar
  18. 18.
    Ghobadi, M., Cheng, Y., Jain, A., Mathis, M.: Trickle: Rate limiting youtube video streaming. Usenix Annual Technical Conference. Boston, pp. 191–196 (2012)Google Scholar
  19. 19.
    Hoquea, M.A., Siekkinena, M., Nurminena, J.K., Aaltob, M., Tarkoma, S.: Mobile multimedia streaming techniques: QoE and energy saving perspective. Pervasive Mobile Comput. 16(Part A), 96–114 (2015)Google Scholar
  20. 20.
    Hoßfeld, T., Egger, S., Schatz, R., Fiedler, M., Masuch, K., Lorentzen, C.: Initial delay vs. interruptions: Between the devil and the deep blue sea. In: 4th International Workshop on Quality of Multimedia Experience (QoMEX). IEEE, Melbourne, pp. 1–6 (2012)Google Scholar
  21. 21.
    Houdaille, R., Gouache, S.: Shaping HTTP adaptive streams for a better user experience. 3rd Multimedia Systems Conference. ACM, Chapel Hill, pp. 1–9 (2012)Google Scholar
  22. 22.
    Jiang, J., Sekar, V., Zhang, H.: Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with festive. In: 8th international conference on Emerging networking experiments and technologies. ACM, pp. 97–108 (2012)Google Scholar
  23. 23.
    Krogfoss, B., Agrawal, A., Sofman, L.: Analytical method for objective scoring of HTTP adaptive streaming (HAS). In: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE, pp. 1–6 (2012)Google Scholar
  24. 24.
    Li, S.Q., Chong, S., Hwang, C.L.: Link capacity allocation and network control by filtered input rate in high-speed networks. IEEE/ACM Trans. Netw. (TON) 3(1), 10–25 (1995)CrossRefGoogle Scholar
  25. 25.
    Mansy, A., Ver Steeg, B., Ammar, M.: Sabre: A client based technique for mitigating the buffer bloat effect of adaptive video flows. In: 4th ACM Multimedia Systems Conference. ACM, pp. 214–225 (2013)Google Scholar
  26. 26.
    Mascolo, S., Grieco, L.A.: Additive increase early adaptive decrease mechanism for TCP congestion control. In: 10th International Conference on Telecommunications (ICT). IEEE, vol. 1, pp. 818–825 (2003)Google Scholar
  27. 27.
    Mascolo, S., Racanelli, G.: Testing TCP Westwood+ over transatlantic links at 10 gigabit/second rate. Protocols for Fast Long-distance Networks (PFLDnet) Workshop. Lyon, pp. 1–6 (2005)Google Scholar
  28. 28.
    Mascolo, S., Casetti, C., Gerla, M., Sanadidi, M.Y., Wang, R.: TCP Westwood: Bandwidth estimation for enhanced transport over wireless links. In: 7th annual international conference on Mobile computing and networking. ACM, pp. 287–297 (2001)Google Scholar
  29. 29.
    Mascolo, S., Grieco, L.A., Ferorelli, R., Camarda, P., Piscitelli, G.: Performance evaluation of Westwood+ TCP congestion control. Perform. Eval. 55(1), 93–111 (2004)Google Scholar
  30. 30.
    Mogul, J.C.: Observing TCP dynamics in real networks. ACM SIGCOMM Comput. Commun. Rev. 22(4) (1992)Google Scholar
  31. 31.
    Ramadan, W., Dedu, E., Bourgeois, J.: Avoiding quality oscillations during adaptive streaming of video. Int. J. Digital Inf. Wireless Commun. (IJDIWC) 1(1), 126–145 (2011)Google Scholar
  32. 32.
    Sandvine: Global internet phenomena report. (2016)
  33. 33.
    Seufert, M., Egger, S., Slanina, M., Zinner, T., Hobfeld, T., Tran-Gia, P.: A survey on quality of experience of HTTP adaptive streaming. Commun. Surv. Tutor. IEEE 17(1), 469–492 (2014)CrossRefGoogle Scholar
  34. 34.
    Shuai, Y., Petrovic, G., Herfet, T.: Olac: An open-loop controller for low-latency adaptive video streaming. IEEE International Conference on Communications (ICC). IEEE, London, pp. 6874–6879 (2015)Google Scholar
  35. 35.
    Villa, B.J., Heegaard, P.E.: Group based traffic shaping for adaptive HTTP video streaming by segment duration control. In: 27th IEEE International Conference on Advanced Information Networking and Applications (AINA). IEEE, pp. 830–837 (2013)Google Scholar
  36. 36.
    Yang, H., Chen, X., Yang, Z., Zhu, X., Chen, Y.: Opportunities and challenges of HTTP adaptive streaming. Int. J. Future Gener. Commun. Netw. 7(6), 165–180 (2014)CrossRefGoogle Scholar
  37. 37.
    Yin, X., Sekar, V., Sinopoli, B.: Toward a principled framework to design dynamic adaptive streaming algorithms over HTTP. 13th ACM Workshop on Hot Topics in Networks. ACM, Los Angeles, pp. 1–9 (2014)Google Scholar
  38. 38.
    Yin, X., Jindal, A., Sekar, V., Sinopoli, B.: A control-theoretic approach for dynamic adaptive video streaming over HTTP. ACM SIGCOMM Comput. Commun. Rev. 45(4), 325–338 (2015)CrossRefGoogle Scholar
  39. 39.
    Zhang, L., Shenker, S., Clark, D.D.: Observations on the dynamics of a congestion control algorithm: The effects of two-way traffic. ACM SIGCOMM Comput. Commun. Rev. 21(4), 133–147 (1991)CrossRefGoogle Scholar
  40. 40.
    Zukerman, M., Neame, T.D., Addie, R.G.: Internet traffic modeling and future technology implications. In: 22nd Annual Joint Conference of the IEEE Computer and Communications (INFOCOM), vol. 1. IEEE, pp. 587–596 (2003)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.INNES, ZAC Atalante ChampeauxRennesFrance
  2. 2.Orange LabsCesson SévignéFrance
  3. 3.IRISA, University of Rennes 1RennesFrance
  4. 4.FEMTO-ST Institute, NuméricaMontbéliardFrance

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