Skip to main content

The capacity of QoE for wireless networks with unreliable transmissions

Abstract

Video streaming is anticipated to dominate wireless traffic in the near future. We study wireless systems where an access point delivers video streams to multiple clients over unreliable wireless channels. The performance of each client is measured by the amount of time that its video playback halts due to buffer underflow, which has been shown to have the most impact on clients’ perceived quality of experience (QoE). This performance measure is significantly different from traditional quality of service metrics. We develop an analytic framework that jointly captures the video playback process and the unreliable and heterogeneous wireless channels. We use a diffusion limit to approximate the short-term QoE performance. We derive the capacity region for QoE by establishing a lower bound of a weighted sum of video halt durations over all clients. We then propose a QoE-optimal policy that can achieve every point within the capacity region. Finally, we compare our policy against two commonly used policies. Both theoretical analysis and simulation results show that our policy greatly outperforms other policies.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Hou, I.-H., Hsieh, P.-C.: QoE-optimal scheduling for on-demand video streams over unreliable wireless networks. In: Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc ’15, pp. 207–216 (2015)

  2. Mok, R., Chan, E., Chang, R.: Measuring the quality of experience of HTTP video streaming. In: IFIP/IEEE International Symposium on Integrated Network Management (IM), pp. 485–492 (2011)

  3. Staelens, N., Moens, S., Van den Broeck, W., Marien, I., Vermeulen, B., Lambert, P., Van De Walle, R., Demeester, P.: Assessing quality of experience of IPTV and video on demand services in real-life environments. IEEE Trans. Broadcast. 56, 458–466 (2010)

    Article  Google Scholar 

  4. Li, X., Wang, C.-C., Lin, X.: On the capacity of immediately-decodable coding schemes for wireless stored-video broadcast with hard deadline constraints. IEEE J. Sel. Areas Commun. 29, 1094–1105 (2011)

    Article  Google Scholar 

  5. Xu, Y., Elayoubi, S., Altman, E., El-Azouzi, R.: Impact of flow-level dynamics on QoE of video streaming in wireless networks. In: Proceedings of IEEE INFOCOM, pp. 2715–2723 (2013)

  6. Chen, H., Yao, D.D.: Fundamentals of Queueing Networks: Performance, Asymptotics, and Optimization. Springer, Berlin (2001)

    Book  Google Scholar 

  7. Hou, I.-H., Borkar, V., Kumar, P.R.: A theory of QoS for wireless. In: Proceedings of of IEEE INFOCOM (2009)

  8. Brown, B.M.: Martingale central limit theorems. Ann. Math. Stat. 42(1), 59–66 (1971)

    Article  Google Scholar 

  9. Harrison, J.M.: Brownian Motion and Stochastic Flow Systems. Wiley, New York (1985)

    Google Scholar 

  10. Hsieh, P.C., Hou, I.H.: Heavy-traffic analysis of QoE optimality for on-demand video streams over fading channels. In: Proceedings of IEEE INFOCOM, pp. 1–9 (2016)

  11. Tassiulas, L., Ephremides, A.: Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Trans. Autom. Control 37(12), 1936–1948 (1992)

    Article  Google Scholar 

  12. Tassiulas, L., Ephremides, A.: Dynamic server allocation to parallel queues with randomly varying connectivity. IEEE Trans. Inf. Theory 39(2), 466–478 (1993)

    Article  Google Scholar 

  13. Csörgő, M.: On the strong law of large numbers and the central limit theorem for martingales. Trans. Am. Math. Soc. 131(1), 259–275 (1968)

    Article  Google Scholar 

  14. Strassen, V.: Almost sure behavior of sums of independent random variables and martingales. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 2: Contributions to Probability Theory, Part 1, pp. 315–343. University of California Press (1967)

  15. ParandehGheibi, A., Médard, M., Ozdaglar, A., Shakkottai, S.: Avoiding interruptions—a qoe reliability function for streaming media applications. IEEE J. Sel. Areas Commun. 29(5), 1064–1074 (2011)

    Article  Google Scholar 

  16. Xu, Y., Altman, E., El-Azouzi, R., Haddad, M., Elayoubi, S., Jimenez, T.: Analysis of buffer starvation with application to objective QoE optimization of streaming services. IEEE Trans. Multimed. 16, 813–827 (2014)

    Article  Google Scholar 

  17. Liang, G.: Effect of delay and buffering on jitter-free streaming over random VBR channels. IEEE Trans. Multimed. 10(6), 1128–1141 (2008)

    Article  Google Scholar 

  18. Jaramillo, J.J., Srikant, R., Ying, L.: Scheduling for optimal rate allocation in ad hoc networks with heterogeneous delay constraints. IEEE J. Sel. Areas Commun. 29(5), 979–987 (2011)

    Article  Google Scholar 

  19. Li, R., Eryilmaz, A.: Scheduling for end-to-end deadline-constrained traffic with reliability requirements in multihop networks. IEEE/ACM Trans. Netw. 20(5), 1649–1662 (2012)

    Article  Google Scholar 

  20. Kim, K.S., Li, C.-P., Modiano, E.: Scheduling multicast traffic with deadlines in wireless networks. In: Proceedings of IEEE INFOCOM, pp. 2193–2201. IEEE (2014)

  21. Kang, X., Wang, W., Jaramillo, J.J., Ying, L.: On the performance of largest-deficit-first for scheduling real-time traffic in wireless networks. In: Proceedings of the Fourteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 99–108. ACM (2013)

  22. Singh, S., Oyman, O., Papathanassiou, A., Chatterjee, D., Andrews, J.G.: Video capacity and QoE enhancements over LTE. In IEEE International Conference on Communications (ICC), pp. 7071–7076. IEEE (2012)

  23. Chandur, P., Sivalingam, K.M.: Quality of experience aware video scheduling in LTE networks. In: Twentieth National Conference on Communications (NCC), pp. 1–6. IEEE (2014)

  24. Bhatia, R., Lakshman, T., Netravali, A., Sabnani, K.: Improving mobile video streaming with link aware scheduling and client caches. In: Proceedings of of IEEE INFOCOM, pp. 100–108. IEEE (2014)

  25. Joseph, V., de Veciana, G.: NOVA: QoE-driven optimization of DASH-based video delivery in networks. In: Proceedings of of IEEE INFOCOM, pp. 82–90 (2014)

  26. Anttonen, A., Mammelaa, A.: Interruption probability of wireless video streaming with limited video lengths. IEEE Trans. Multimed. 16, 1176–1180 (2014)

    Article  Google Scholar 

  27. Yang, J., Hu, H., Xi, H., Hanzo, L.: Online buffer fullness estimation aided adaptive media playout for video streaming. IEEE Trans. Multimed. 13(5), 1141–1153 (2011)

    Article  Google Scholar 

  28. Kingman, J.F.C.: On queues in heavy traffic. J. R. Stat. Soc. Ser. B (Methodol.) 24(2), 383–392 (1962)

    Google Scholar 

  29. Iglehart, D.L., Whitt, W.: Multiple channel queues in heavy traffic. I. Adv. Appl. Probab. 2(1), 150–177 (1970)

    Article  Google Scholar 

  30. Whitt, W.: Weak convergence theorems for priority queues: preemptive-resume discipline. J. Appl. Probab. 8(1), 74–94 (1971)

    Article  Google Scholar 

  31. Foschini, G., Salz, J.: A basic dynamic routing problem and diffusion. IEEE Trans. Commun. 26, 320–327 (1978)

    Article  Google Scholar 

  32. Williams, R.: Diffusion approximations for open multiclass queueing networks: sufficient conditions involving state space collapse. Queueing Syst. 30, 27–88 (1998)

    Article  Google Scholar 

  33. Bramson, M.: State space collapse with application to heavy traffic limits for multiclass queueing networks. Queueing Syst. Theory Appl. 30, 89–148 (1998)

    Article  Google Scholar 

  34. Harrison, J.M.: Heavy traffic analysis of a system with parallel servers: asymptotic optimality of discrete-review policies. Ann. Appl. Probab. 8, 822–848 (1998)

    Article  Google Scholar 

  35. Harrison, J., Löpez, M.: Heavy traffic resource pooling in parallelserver systems. Queueing Syst. 33(4), 339–368 (1999)

    Article  Google Scholar 

  36. Stolyar, A.L.: MaxWeight scheduling in a generalized switch: state space collapse and workload minimization in heavy traffic. Ann. Appl. Probab. 14, 1–53 (2004)

    Article  Google Scholar 

  37. Shakkottai, S., Srikant, R., Stolyar, A.L.: Pathwise optimality of the exponential scheduling rule for wireless channels. Adv. Appl. Probab. 36(4), 1021–1045 (2004)

    Article  Google Scholar 

  38. Eryilmaz, A., Srikant, R.: Asymptotically tight steady-state queue length bounds implied by drift conditions. Queueing Syst. Theory Appl. 72, 311–359 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

This material is based upon work supported in part by the US Army Research Laboratory and the US Army Research Office under contract/Grant Number W911NF-15-1-0279 and NPRP Grant 8-1531-2- 651 of Qatar National Research Fund (a member of Qatar Foundation).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to I-Hong Hou.

Additional information

A preliminary version of this work appears in the proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing (ACM MobiHoc’ 15) [1].

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hou, IH., Hsieh, PC. The capacity of QoE for wireless networks with unreliable transmissions. Queueing Syst 87, 131–159 (2017). https://doi.org/10.1007/s11134-017-9527-0

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11134-017-9527-0

Keywords

  • Wireless networks
  • Capacity
  • Packet scheduling
  • QoE
  • Video streaming
  • Diffusion limit

Mathematics Subject Classification

  • 60K25
  • 60J65
  • 90B22