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An encoding-aware bitrate adaptation mechanism for video streaming over HTTP

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Abstract

The great interest in flix-like services has amplified multimedia traffic over the Internet. Recently released traffic forecasting predicts that video-related traffic will be responsible for the majority of Internet traffic by 2022. Such traffic will come in a wide range of duration and in the two modes of live and on-demand. Additionally, it is expected to scale and deliver a smoothed experience to an already fragmented audience. The adaptive bitrate over Hypertext Transfer Protocol (HTTP) has emerged as the top technology for multimedia content transport and delivery. Despite the large amount of work in this area, running streaming applications on overload channels still demands the development of effective strategies. In this work, a video bitrate adaptation mechanism deployed in an overloaded channel of an access network is proposed and evaluated under live and on-demand service modes. This mechanism makes decisions regarding bitrate switching based on the Quality of Experience (QoE)-related parameters to accommodate conflicting variables of its design space, namely, image quality, session continuity and short play time. To evaluate this mechanism, a multifactor QoE metric is proposed based on session parameters such as stalls, startup delay, image quality and the mechanism bitrate misalignment. Moreover, in the numerical studies for the evaluation of the effectiveness of the proposed mechanism, the average video bitrate, instability and fairness are measured. Overall, the proposed mechanism was able to improve the session QoE for both live and on-demand modes.

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References

  1. Ayad I, Im Y, Keller E, Ha S (2018) A practical evaluation of rate adaptation algorithms in http-based adaptive streaming. Comput Netw 133:90–103. https://doi.org/10.1016/j.comnet.2018.01.019

    Article  Google Scholar 

  2. Bampis C, Bovik A (2018) Feature-based prediction of streaming video qoe: distortions, stalling and memory. Signal Process Image Commun 68:218–228. https://doi.org/10.1016/j.image.2018.05.017

    Article  Google Scholar 

  3. Barman N, Martini MG (2019) QoE modeling for HTTP adaptive video streaming: A survey and open challenges. IEEE Access 7:30831–30859. https://doi.org/10.1109/ACCESS.2019.2901778

    Article  Google Scholar 

  4. Basso S, Servetti A, Masala E, De Martin JC (2014) Measuring dash streaming performance from the end users perspective using neubot. In: Proceedings of the 5th ACM Multimedia Systems Conference, MMSys ’14. https://doi.org/10.1145/2557642.2563671. ACM, pp 1–6

  5. Beben A, Wiśniewski P, Batalla JM, Krawiec P (2016) Abma+: Lightweight and efficient algorithm for http adaptive streaming. In: Proceedings of the 7th International Conference on Multimedia Systems, MMSys ’16. https://doi.org/10.1145/2910017.2910596. ACM, pp 2:1–2:11

  6. Bentaleb A, Taani B, Begen AC, Timmerer C, Zimmermann R (2019) A survey on bitrate adaptation schemes for streaming media over http. IEEE Commun Surv Tutorials 21:562–585. https://doi.org/10.1109/COMST.2018.2862938

    Article  Google Scholar 

  7. Bouraqia K, Sabir E, Sadik M, Ladid L (2020) Quality of experience for streaming services: Measurements, challenges and insights. IEEE Access 8:13341–13361. https://doi.org/10.1109/ACCESS.2020.2965099

    Article  Google Scholar 

  8. Brown MA (2006) Traffic control HOWTO. Accessed: 2021-01-19

  9. Bulkan U, Dagiuklas T (2019) Predicting quality of experience for online video service provisioning. Multimed Tools Appl 78(13). https://doi.org/10.1007/s11042-019-7164-9

  10. Cisco (2018) Cisco visual networking index (vni) complete forecast update, 2017–2022. Accessed: 2021-01-19

  11. Corporation A (2020) HTTP live streaming overview. Accessed: 2021-01-19

  12. Coelho MdS, Melo CAV, da Fonseca NLS (2017) Versioning-aware and qoe-oriented strategy for adaptative bitrate streaming. In: IEEE International Conference on Communications (ICC). https://doi.org/10.1109/ICC.2017.7996743, pp 1–6

  13. ISO/IEC 23009-1:2019 information technology – dynamic adaptive streaming over http (dash) – part 1: Media presentation description and segment formats, 4 edn. International Organization for Standardization/International Electrotechnical Commission, Geneva, Switzerland (2019)

  14. Jiang J, Sekar V, Zhang H (2014) Improving fairness, efficiency, and stability in http-based adaptive video streaming with festive. IEEE/ACM Trans Netw 22:326–340. https://doi.org/10.1109/TNET.2013.2291681

    Article  Google Scholar 

  15. Jiang J, Sun S, Sekar V, Zhang H (2017) Pytheas: Enabling data-driven quality of experience optimization using group-based exploration-exploitation. In: Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation, NSDI’17. USENIX Association, USA, pp 393–406

  16. Juluri P, Tamarapalli V, Medhi D (2015) Sara: Segment aware rate adaptation algorithm for dynamic adaptive streaming over http. In: 2015 IEEE International Conference on Communication Workshop (ICCW). https://doi.org/10.1109/ICCW.2015.7247436, pp 1765–1770

  17. Juluri P, Tamarapalli V, Medhi D (2016) Measurement of quality of experience of video-on-demand services: A survey. IEEE Commun Surv Tutorials 18:401–418. https://doi.org/10.1109/COMST.2015.2401424

    Article  Google Scholar 

  18. Mao H, Netravali R, Alizadeh M (2017) Neural adaptive video streaming with pensieve. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication, SIGCOMM ’17. https://doi.org/10.1145/3098822.3098843. ACM, pp 197–210

  19. Murugadoss R, Kumar MMVM (2020) The quality of experience framework for http adaptive streaming algorithm in video streaming over wireless networks. Int J Fut Gener Commun Netw 13:1491–1502

    Google Scholar 

  20. Pantos R, May W (2017) HTTP Live Streaming. RFC 8216. https://doi.org/10.17487/RFC8216

  21. Petrangeli S, Wu T, Wauters T, Huysegems R, Bostoen T, De Turck F (2017) A machine learning-based framework for preventing video freezes in http adaptive streaming. J Netw Comput Appl 94:78–92. https://doi.org/10.1016/j.jnca.2017.07.009

    Article  Google Scholar 

  22. Spiteri K, Urgaonkar R, Sitaraman RK (2016) Bola: Near-optimal bitrate adaptation for online videos. In: IEEE INFOCOM - The 35th Annual IEEE International Conference on Computer Communications. https://doi.org/10.1109/INFOCOM.2016.7524428, pp 1–9

  23. Sun L, Zong T, Liu Y, Wang Y, Zhu H (2019) Optimal strategies for live video streaming in the low-latency regime. In: IEEE 27th International Conference on Network Protocols (ICNP). https://doi.org/10.1109/ICNP.2019.8888127, pp 1–4

  24. Thang TC, Le HT, Pham AT, Ro YM (2014) An evaluation of bitrate adaptation methods for http live streaming. IEEE J Sel Areas Commun 32:693–705. https://doi.org/10.1109/JSAC.2014.140403

    Article  Google Scholar 

  25. Tian G, Liu Y (2016) Towards agile and smooth video adaptation in http adaptive streaming. IEEE/ACM Trans Netw 24:2386–2399. https://doi.org/10.1109/TNET.2015.2464700

    Article  Google Scholar 

  26. Wilk S, Stohr D, Effelsberg W (2016) A content-aware video adaptation service to support mobile video. ACM Trans Multimed Comput Commun Appl 12:82:1–82:23. https://doi.org/10.1145/2983636

    Google Scholar 

  27. Wisniewski P, Batalla JM, Beben A, Krawiec P, Chydzinski A (2017) On optimizing adaptive algorithms based on rebuffering probability. ACM Trans Multimed Comput Commun Appl 13:43:1–43:20. https://doi.org/10.1145/3092837

    Article  Google Scholar 

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Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 and by the Fundação de Amparo à Pesquisa do Estado do Amazonas - Finance Code 024/2014.

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Authors and Affiliations

  1. Institute of Computing, Federal University of Amazonas, Manaus, Brazil

    Maiara Souza Coelho & Cesar A. V. Melo

  2. Institute of Computing, State University of Campinas, Campinas, Brazil

    Nelson L. S. da Fonseca

Authors
  1. Maiara Souza Coelho
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  2. Cesar A. V. Melo
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  3. Nelson L. S. da Fonseca
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Correspondence to Maiara Souza Coelho.

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Coelho, M.S., Melo, C.A.V. & Fonseca, N.L.S.d. An encoding-aware bitrate adaptation mechanism for video streaming over HTTP. Multimed Tools Appl 81, 27423–27451 (2022). https://doi.org/10.1007/s11042-022-12520-z

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