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Signal, Image and Video Processing

, Volume 13, Issue 7, pp 1367–1375 | Cite as

Measuring bandwidth and buffer occupancy to improve the QoE of HTTP adaptive streaming

  • Nabin Kumar KarnEmail author
  • Hongli Zhang
  • Feng Jiang
  • Rahul Yadav
  • Asif Ali Laghari
Original Paper
  • 135 Downloads

Abstract

Live and on-demand video streaming service systems consume significant portion of Internet traffic all over the world. HTTP adaptive streaming is becoming the de-facto standard for adaptive video streaming solutions. Conventional estimation scheme for bandwidth estimation is not appropriate to estimate the bandwidth when multiple clients compete for a common bottleneck link, due to the ON–OFF traffic pattern. They overestimate the network bandwidth which leads to degradation of quality of experience by unnecessary changes in video quality, average video quality and unfairness of video quality. In this paper, we proposed receiver-side bandwidth-measured method to achieve a better quality of experience in multiple-client scenario. The proposed method estimates the obtainable network bandwidth based on the buffer status and segment throughput. The video buffer model is associated with three thresholds (i.e., one for initial start-up and two for operating thresholds). NS-3 network simulator has been deployed to measure the performance of HTTP adaptive streaming. Simulation outputs reflect that the proposed method enhances the quality of experience than conventional methods.

Keywords

HTTP Adaptive streaming Quality of experience Multiple clients Bandwidth estimation Fairness 

Notes

References

  1. 1.
    Cisco: VNI global mobile data traffic forecast update, 2014–2019 (2015)Google Scholar
  2. 2.
    Seufert, M., Egger, S., Slanina, M., Zinner, T., Hoßfeld, T., Tran-Gia, P.: A survey on quality of experience of HTTP adaptive streaming. IEEE Commun. Surv. Tutor. 17(1), 469–492 (2015)CrossRefGoogle Scholar
  3. 3.
    Zhao, S., Gao, Y., Ding, G., Chua, T.S.: Real-time multimedia social event detection in microblog. IEEE Trans. Cybern. 48(11), 3218–3231 (2018)CrossRefGoogle Scholar
  4. 4.
    Akhshabi, S., Anantakrishnan, L., Begen, A.C., Dovrolis, C.: What happens when HTTP adaptive streaming players compete for bandwidth? In: NOSSDAV '12 Proceedings of the 22nd international workshop on Network and Operating System Support for Digital Audio and Video, pp. 9–14. ACM, Toronto, Canada (2012).  https://doi.org/10.1145/2229087.2229092 CrossRefGoogle Scholar
  5. 5.
    Begen, A., Akgul, T., Baugher, M.: Watching video over the web: Part 1: streaming protocols. IEEE Internet Comput. 15(2), 54–63 (2011)CrossRefGoogle Scholar
  6. 6.
    Hussain, M., Hameed, A., Hameed, A.: Adaptive video-aware forward error correction code allocation for reliable video transmission. Signal Image Video Process. 12, 161–169 (2018)CrossRefGoogle Scholar
  7. 7.
    Karn, N.K., Zhang, H., Jiang, F.: User-perceived quality aware adaptive streaming of 3D multi-view video plus depth over the internet. Multimed. Tools Appl. 77(17), 22965–22983 (2018)CrossRefGoogle Scholar
  8. 8.
    Balaji, L., Thyagharajan, K.K.: An enhanced performance for H.265/SHVC based on combined AEGBM3D filter and back-propagation neural network. Signal Image Video Process. 12(5), 809–817 (2018)CrossRefGoogle Scholar
  9. 9.
    Ren, P., Yang, F., Li, W.: Rate model considering inter-symbol dependency for HEVC inter-frame coding. Signal Image Video Process. 11(4), 643–650 (2017)CrossRefGoogle Scholar
  10. 10.
    Hong, S., Yang, D., Park, B., Yu, S.: An efficient intra-mode decision method for HEVC. Signal Image Video Process. 10(6), 1055–1063 (2016)CrossRefGoogle Scholar
  11. 11.
    Ahmed, A.A.: An optimal complexity H.264/AVC encoding for video streaming over next generation of wireless multimedia sensor networks. Signal Image Video Process. 10(6), 1143–1150 (2016)CrossRefGoogle Scholar
  12. 12.
    Zhao, S., Yao, H., Gao, Y., Ding, G., Chua, T.S.: Predicting personalized image emotion perceptions in social networks. IEEE Trans. Affect. Comput. 9(4), 526–540 (2018)CrossRefGoogle Scholar
  13. 13.
    Zhao, S., Yao, H., Gao, Y., Ji, R., Ding, G.: Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Trans. Multimed. 19(3), 632–645 (2017)CrossRefGoogle Scholar
  14. 14.
    Zhao, S., et al.: Discrete probability distribution prediction of image emotions with shared sparse learning. IEEE Trans. Affect. Comput. (2018).  https://doi.org/10.1109/TAFFC.2018.2818685 CrossRefGoogle Scholar
  15. 15.
    Zhao, S., Yao, H., Zhao, S., Jiang, X., Jiang, X.: Multi-modal microblog classification via multi-task learning. Multimed. Tools Appl. 75(15), 8921–8938 (2016)CrossRefGoogle Scholar
  16. 16.
    Wang, F., Qi, S., Gao, G., Zhao, S., Wang, X.: Logo information recognition in large-scale social media data. Multimed. Syst. 22(1), 63–73 (2016)CrossRefGoogle Scholar
  17. 17.
    Joseph, D., et al.: Understanding the impact of video quality on user engagement. Commun. ACM 56(3), 91–99 (2013)CrossRefGoogle Scholar
  18. 18.
    Liu, C., Bouazizi, I., Gabbouj, M.: Rate adaptation for adaptive HTTP streaming. In: MMSys '11 Proceedings of the second annual ACM conference on Multimedia systems, pp. 169–174. ACM, San Jose, CA (2011).  https://doi.org/10.1145/1943552.1943575 CrossRefGoogle Scholar
  19. 19.
    Thang, T.C., Ho, Q.D., Kang, J.W., Pham, A.T.: Adaptive streaming of audiovisual content using MPEG DASH. IEEE Trans. Consum. Electron. 58(1), 78–85 (2012)CrossRefGoogle Scholar
  20. 20.
    Mok, R.K.P., Luo, X., Chan, E.W.W., Chang, R.K.C.: QDASH : A QoE-aware DASH system. In: Proceedings of the Third Annual ACM Conference on Multimedia Systems (2012)Google Scholar
  21. 21.
    Akhshabi, S., Begen, A.C., Dovrolis, C.: An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP. In: Proceedings of ACM Conference on Multimedia System, pp. 157–168 (2011)Google Scholar
  22. 22.
    Dubin, R., Hadar, O., Dvir, A.: The effect of client buffer and MBR consideration on DASH adaptation logic. In: IEEE Wireless Communications and Networking Conference, WCNC (2013)Google Scholar
  23. 23.
    VideoLAN-VLC media player source code. [Online]. Available: http://www.videolan.org/vlc/download-sources.html (2013)
  24. 24.
    Dolinsky, K., Celikovsky, S.: Kalman filter under nonlinear system transformations. In: Proceedings of 2012 American Control Conference (ACC), pp. 4789–4794. IEEE, Montreal, QC (2012).  https://doi.org/10.1109/ACC.2012.6315366 CrossRefGoogle Scholar
  25. 25.
    Jiang, J., Sekar, V., Zhang, H.: Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with festive. IEEE/ACM Trans. Netw. 22, 326–340 (2014)CrossRefGoogle Scholar
  26. 26.
    The network simulator 3 [Online]. Available: http://www.nsnam.org.” [Online]. Available: https://www.nsnam.org/. Accessed: 22 Feb 2019
  27. 27.
    Zambelli, A.: IIS Smooth Streaming Technical, Microsoft Corporation, pp. 1–17. https://www.bogotobogo.com/VideoStreaming/Files/iis8/IIS_Smooth_Streaming_Technical_Overview.pdf (2009)

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Nabin Kumar Karn
    • 1
    Email author
  • Hongli Zhang
    • 1
  • Feng Jiang
    • 1
  • Rahul Yadav
    • 1
  • Asif Ali Laghari
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina

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