Server-side Segment Selection For Low-latency Streaming - S4S
Metadata
- Publisher
- SMPTE — White Plains, NY, USA
- Doc Type
- Conference Paper
- Content Type
- Original Research
- Volume
- 2020, No. 11, pp. 1–10
- Abstract
- The OTT streaming growth is driven by the increasing number of wireless connected devices such as tablet and phones. An enabler for using these devices is ABR streaming (Adaptive Bitrate) that allows them to select at each moment the stream quality that best fits the available bandwidth. The overall experience is highly dependent on the bandwidth estimation. The current approach to bandwidth estimation in HTTP-based ABR players is challenged by the evolution toward low latency protocols (DASH CTE) or (HLS LL) which result in micro-burst of traffic. Indeed, estimation at the client-side, at the HTTP level, relies on the assumption that a relatively large segment of data is available and can be sent at the link-speed which is not the case anymore with low latency protocols. — To address this issue, we propose a novel scheme S4S, that allows players to interact with an S4S-enabled server in order to improve the overall experience. First, bandwidth estimation is done at the server-side, using transport's congestion control sender-side stats, leading to more precise estimates even in the presence of small burst, especially when using modern congestion control algorithms such as BBR or PCC. Second, we define a protocol for allowing the Network to control the bandwidth versus quality tradeoff.
- Publication Date
- 2020-11-10
- DOI
10.5594/M001926- Link
- https://doi.org/10.5594/M001926
- Author(s)
- Guillaume BichotBroadpeakNicolas Le ScouarnecBroadpeak
- Keyword(s)
- Low-latency Streaming
- Copyright
- © 2020 Society of Motion Picture and Television Engineers, Inc.
Bibliographic Reference(s)
- ETSI.TS-103-285.2020 — European Telecommunications Standards Institute (ETSI) TS 103 285 V1.3.1 (2020-02), Digital Video Broadcasting (DVB); EXTERNAL
- Mao H., Netravali R., Alizadeh M., “Neural Adaptive Video Streaming with Pensieve”, SIGCOMM ’17, 2017, Los Angeles, CA, USA. EXTERNAL
- Petrangeli S., Famaey J., Claeys M., Latré S., De Turk F., “QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming”, ACM Trans. Multimedia Comput. Commun. October 2015, Vol. 12, No. 2, Article 28. EXTERNAL
- ISO/IEC23009, “Information technology— Dynamic adaptive streaming over HTTP (DASH)” EXTERNAL
- M. Lim, M. N. Akcay, A. Bentaleb, A. C. Begen and R. Zimmermann, “When They Go High, We Go Low: Low-Latency Live Streaming in dash.js with LoL”, MMSys '20,: Proceedings of the 11th ACM Multimedia Systems Conference. May 2020 Pages 321–326 EXTERNAL
- Mok R. K. P., Luo X. , Chan E. W. W. , Chang R. K. C., “QDASH: A QoE-aware DASH system”, MMSys’12, February 22-24 2012, Chapel Hill, North Carolina, USA. EXTERNAL
- “Guideline for implementation: DASH-IF interoperability points. Change request for low latency modes for Dash”, https://dashif.org/guidelines/#agreed-crs-for-next-version EXTERNAL
- Khan K., Goodbridge W., “Server-based and Network-assisted Solutions for Adaptive Video Streaming”, Int. J. Advanced Networking and Applications, Vol. 09, No. 03, 2017, PP. 3432- EXTERNAL
- El Essaili A, Schroeder D, Staehle D, Shehada M, Kellerer W, Steinbach E, “Quality-of-Experience driven Adaptive HTTP Media Delivery”, IEEE International Conference on Communications (ICC 2013), June 2013. EXTERNAL
- G. Bichot, P.J. Guery, N. Le Scouarnec, “How To Optimize ABR Video Delivery With Server-Side Quality Control”, Proceedings of the 2020 NAB Broadcast Engineering and Information Technology Conference. May 13 2020. EXTERNAL
- T. Karagkioules, R. Mekuria, D. Griffioen, A. Wagenaar, “Online learning for low-latency adaptive streaming“, MMSys '20,: Proceedings of the 11th ACM Multimedia Systems Conference. May 2020 Pages 315–320 EXTERNAL
- De Cicco L, Mascolo S., Palmisano V., 2011. “Feedback control for adaptive live video streaming”, 2nd Annual ACM Conference on Multimedia Systems (MMSys’11). ACM, pp.145–156. EXTERNAL
- ISO/IEC23000-19, “Information technology — Multimedia application format (MPEG-A) —Part 19:Common media application format (CMAF) for segmented media” EXTERNAL
- Grand Challenge on Adaptation Algorithms for Near-Second Latency. In ACM MMSys, 2020. EXTERNAL
- Bentaleb A., Timmerer C. Begen A. C., Zimmermann R., “Bandwidth Prediction in Low-Latency Chunked Streaming” NOSSDAV’19, June 21, 2019, Amherst, MA, USA. EXTERNAL
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Guillaume Bichot and Nicolas Le Scouarnec; Server-side Segment Selection For Low-latency Streaming - S4S, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020. Available at https://doi.org/10.5594/M001926
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Guillaume Bichot and Nicolas Le Scouarnec; Server-side Segment Selection For Low-latency Streaming - S4S, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020. Available at https://doi.org/10.5594/M001926
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Guillaume Bichot and Nicolas Le Scouarnec; Server-side Segment Selection For Low-latency Streaming - S4S, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020. Available at https://doi.org/10.5594/M001926
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<span class="citation">Guillaume Bichot and Nicolas Le Scouarnec; <cite>Server-side Segment Selection For Low-latency Streaming - S4S</cite>, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020. Available at <a href="https://doi.org/10.5594/M001926" target="_blank" rel="noopener">https://doi.org/10.5594/M001926</a></span>
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Guillaume Bichot and Nicolas Le Scouarnec; Server-side Segment Selection For Low-latency Streaming - S4S, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020
doi: 10.5594/M001926
url: https://doi.org/10.5594/M001926
doi: 10.5594/M001926
url: https://doi.org/10.5594/M001926
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<li> Guillaume Bichot and Nicolas Le Scouarnec; <cite id="bib-10-5594-m001926">Server-side Segment Selection For Low-latency Streaming - S4S</cite>, SMPTE Meetings and Conferences ( November 2020); SMPTE, 2020 <span class="doi">10.5594/M001926</span> </li>