skip to main content
research-article

Performance Analysis of ACTE: A Bandwidth Prediction Method for Low-latency Chunked Streaming

Published:21 June 2020Publication History
Skip Abstract Section

Abstract

HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency. This allows media segments to be delivered while still being packaged. However, conventional schemes often make widely inaccurate bandwidth measurements due to the presence of idle periods between the chunks and hence this is causing sub-optimal adaptation decisions. To address this issue, we earlier proposed ACTE (ABR for Chunked Transfer Encoding) [6], a bandwidth prediction scheme for low-latency chunked streaming. While ACTE was a significant step forward, in this study we focus on two still remaining open areas, namely, (i) quantifying the impact of encoding parameters, including chunk and segment durations, bitrate levels, minimum interval between IDR-frames and frame rate on ACTE, and (ii) exploring the impact of video content complexity on ACTE. We thoroughly investigate these questions and report on our findings. We also discuss some additional issues that arise in the context of pursuing very low latency HTTP video streaming.

References

  1. ISO/IEC. 2018. Information Technology—Multimedia Application Format (MPEG-A)—Part 19: Common Media Application Format (CMAF) for Segmented Media. Standard ISO/IEC 23000-19:2018. ISO/IEC, Geneva, CH. Retrieved from https://www.iso.org/standard/71975.html.Google ScholarGoogle Scholar
  2. Apple. 2019. Protocol Extension for Low-Latency HLS. Retrieved from https://developer.apple.com/documentation/http_live_streaming/protocol_extension_for_low-latency_hls_preliminary_specification.Google ScholarGoogle Scholar
  3. A. C. Begen and Y. Syed. 2018. Are the streaming format wars over? In Proceedings of the IEEE International Conference on Multimedia Expo Workshops (ICMEW’18). 1--4. DOI:https://doi.org/10.1109/ICMEW.2018.8551563Google ScholarGoogle Scholar
  4. Abdelhak Bentaleb, Ali C. Begen, Saad Harous, and Roger Zimmermann. 2018. Want to play DASH? A game theoretic approach for adaptive streaming over HTTP. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys’18). ACM, New York, NY, 13--26. DOI:https://doi.org/10.1145/3204949.3204961Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, and R. Zimmermann. 2019. A survey on bitrate adaptation schemes for streaming media over HTTP. IEEE Commun. Surveys Tutor. 21, 1 (2019), 562--585.Google ScholarGoogle ScholarCross RefCross Ref
  6. Abdelhak Bentaleb, Christian Timmerer, Ali C. Begen, and Roger Zimmermann. 2019. Bandwidth prediction in low-latency chunked streaming. In Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV’19). ACM, New York, NY, 7--13. DOI:https://doi.org/10.1145/3304112.3325611Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. N. Bouzakaria, C. Concolato, and J. Le Feuvre. 2014. Overhead and performance of low latency live streaming using MPEG-DASH. In Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications. 92--97. DOI:https://doi.org/10.1109/IISA.2014.6878732Google ScholarGoogle Scholar
  8. DASH-IF and DVB. 2019. Low-latency Modes for DASH. Retrieved from https://dashif.org/docs/DASH-IF-IOP-CR-Low-Latency-Live-Community-Review.pdf.Google ScholarGoogle Scholar
  9. DASH Industry Forum (DASH-IF). 2019. dash.js JavaScript Reference Client. Retrieved from https://reference.dashif.org/dash.js/.Google ScholarGoogle Scholar
  10. A. E. Essaili, T. Lohmar, and M. Ibrahim. 2018. Realization and evaluation of an end-to-end low latency live DASH system. In Proceedings of the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB’18). 1--5. DOI:https://doi.org/10.1109/BMSB.2018.8436922Google ScholarGoogle Scholar
  11. R. J. Haddad, M. P. McGarry, and P. Seeling. 2013. Video bandwidth forecasting. IEEE Commun. Surveys Tutor. 15, 4 (2013), 1803--1818. DOI:https://doi.org/10.1109/SURV.2013.032213.00091Google ScholarGoogle ScholarCross RefCross Ref
  12. Simon S. Haykin. 2008. Adaptive Filter Theory. Pearson Education India.Google ScholarGoogle Scholar
  13. P. Houzé, E. Mory, G. Texier, and G. Simon. 2016. Applicative-layer multipath for low-latency adaptive live streaming. In Proceedings of the IEEE International Conference on Communications (ICC’16). 1--7. DOI:https://doi.org/10.1109/ICC.2016.7511550Google ScholarGoogle Scholar
  14. Xinjue Hu, Wei Quan, Tao Guo, Yu Liu, and Lin Zhang. 2019. Mobile edge assisted live streaming system for omnidirectional video. Mobile Info. Syst. 2019 (2019).Google ScholarGoogle Scholar
  15. Te-Yuan Huang, Ramesh Johari, Nick McKeown, Matthew Trunnell, and Mark Watson. 2014. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. In Proceedings of the Association for Computing Machinery’s Special Interest Group on Data Communications (SIGCOMM’14). Association for Computing Machinery, New York, NY, 187--198. DOI:https://doi.org/10.1145/2619239.2626296Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Bitmovin Inc. 2019. Video Developer Report. Retrieved from https://bitmovin.com/bitmovin-2019-video-developer-report-av1-codec-ai-machine-learning-low-latency/.Google ScholarGoogle Scholar
  17. Will L. [n.d.]. Ultra-Low-Latency Streaming Using Chunked-Encoded and Chunked-Transferred CMAF. Akamai White paper. Retrieved from https://www.akamai.com/us/en/multimedia/documents/white-paper/low-latency-streaming-cmaf-whitepaper.pdf.Google ScholarGoogle Scholar
  18. Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran. 2014. Probe and adapt: Rate adaptation for HTTP video streaming at scale. IEEE J. Select. Areas Commun. 32, 4 (Apr. 2014), 719--733. DOI:https://doi.org/10.1109/JSAC.2014.140405Google ScholarGoogle ScholarCross RefCross Ref
  19. Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural adaptive video streaming with pensieve. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM’17). ACM, New York, NY, 197--210. DOI:https://doi.org/10.1145/3098822.3098843Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore. 2019. An overview on application of machine learning techniques in optical networks. IEEE Commun. Surveys Tutor. 21, 2 (2019), 1383--1408. DOI:https://doi.org/10.1109/COMST.2018.2880039Google ScholarGoogle ScholarCross RefCross Ref
  21. R. K. Naha, S. Garg, D. Georgakopoulos, P. P. Jayaraman, L. Gao, Y. Xiang, and R. Ranjan. 2018. Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE Access 6 (2018), 47980--48009. DOI:https://doi.org/10.1109/ACCESS.2018.2866491Google ScholarGoogle ScholarCross RefCross Ref
  22. Michael J. Neely. 2010. Queue stability and probability 1 convergence via lyapunov optimization. arXiv preprint arXiv:1008.3519.Google ScholarGoogle Scholar
  23. H. Pang, C. Zhang, F. Wang, J. Liu, and L. Sun. 2019. Towards low latency multi-viewpoint 360° interactive video: A multimodal deep reinforcement learning approach. In Proceedings of the IEEE Conference on Computer Communications. 991--999. DOI:https://doi.org/10.1109/INFOCOM.2019.8737395Google ScholarGoogle Scholar
  24. R. Pantos. 2019. HTTP Live Streaming 2nd Edition. Retrieved from https://datatracker.ietf.org/doc/draft-pantos-hls-rfc8216bis/.Google ScholarGoogle Scholar
  25. Twitter Periscope. 2018. Introducing LHLS Media Streaming. Retrieved from https://medium.com/@periscopecode/introducing-lhls-media-streaming-eb6212948bef.Google ScholarGoogle Scholar
  26. Darijo Raca, Ahmed H. Zahran, Cormac J. Sreenan, Rakesh K. Sinha, Emir Halepovic, Rittwik Jana, Vijay Gopalakrishnan, Balagangadhar Bathula, and Matteo Varvello. 2019. Empowering video players in cellular: Throughput prediction from radio network measurements. In Proceedings of the 10th ACM Multimedia Systems Conference (MMSys’19). ACM, New York, NY, 201--212. DOI:https://doi.org/10.1145/3304109.3306233Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. W. Robitza, S. Göring, A. Raake, D. Lindegren, G. Heikkilä, J. Gustafsson, P. List, B. Feiten, U. Wüstenhagen, M.-N. Garcia, K. Yamagishi, and S. Broom. 2018. HTTP adaptive streaming QoE estimation with ITU-T Rec. P. 1203: Open databases and software. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys’18). ACM, New York, NY, 466--471. DOI:https://doi.org/10.1145/3204949.3208124Google ScholarGoogle Scholar
  28. Y. Shuai and T. Herfet. 2018. Towards reduced latency in adaptive live streaming. In Proceedings of the 15th IEEE Annual Consumer Communications Networking Conference (CCNC’18). 1--4. DOI:https://doi.org/10.1109/CCNC.2018.8319262Google ScholarGoogle Scholar
  29. I. Sodagar. 2011. The MPEG-DASH standard for multimedia streaming over the internet. IEEE MultiMedia 18, 4 (April 2011), 62--67. DOI:https://doi.org/10.1109/MMUL.2011.71Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. K. Spiteri, R. Urgaonkar, and R. K. Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications. 1--9. DOI:https://doi.org/10.1109/INFOCOM.2016.7524428Google ScholarGoogle Scholar
  31. Yi Sun, Xiaoqi Yin, Junchen Jiang, Vyas Sekar, Fuyuan Lin, Nanshu Wang, Tao Liu, and Bruno Sinopoli. 2016. CS2P: Improving video bitrate selection and adaptation with data-driven throughput prediction. In Proceedings of the ACM SIGCOMM Conference (SIGCOMM’16). ACM, New York, NY, 272--285. DOI:https://doi.org/10.1145/2934872.2934898Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. V. Swaminathan and S. Wei. 2011. Low latency live video streaming using HTTP chunked encoding. In Proceedings of the IEEE 13th International Workshop on Multimedia Signal Processing. 1--6. DOI:https://doi.org/10.1109/MMSP.2011.6093825Google ScholarGoogle Scholar
  33. J. Van Der Hooft, S. Petrangeli, T. Wauters, R. Huysegems, T. Bostoen, and F. De Turck. 2018. An HTTP/2 push-based approach for low-latency live streaming with super-short segments. J. Netw. Syst. Manage. 26, 1 (Jan. 2018), 51--78. DOI:https://doi.org/10.1007/s10922-017-9407-2Google ScholarGoogle Scholar
  34. V. Veillon, C. Denninnart, and M. A. Salehi. 2019. F-FDN: Federation of fog computing systems for low latency video streaming. In Proceedings of the IEEE 3rd International Conference on Fog and Edge Computing (ICFEC’19). 1--9. DOI:https://doi.org/10.1109/CFEC.2019.8733154Google ScholarGoogle Scholar
  35. Xiufeng Xie, Xinyu Zhang, Swarun Kumar, and Li Erran Li. 2016. piStream: Physical layer informed adaptive video streaming over LTE. GetMobile: Mobile Comp. and Comm. 20, 2 (Oct. 2016), 31--34. DOI:https://doi.org/10.1145/3009808.3009819Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Mariem Ben Yahia, Yannick Le Louedec, Gwendal Simon, Loutfi Nuaymi, and Xavier Corbillon. 2019. HTTP/2-based frame discarding for low-latency adaptive video streaming. ACM Trans. Multimedia Comput. Commun. Appl. 15, 1 (Feb. 2019). DOI:https://doi.org/10.1145/3280854Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. X. Yin, A. Jindal, V. Sekar, and B. Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. SIGCOMM Comput. Commun. Rev. 45, 4 (Aug. 2015), 325--338. DOI:https://doi.org/10.1145/2829988.2787486Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Performance Analysis of ACTE: A Bandwidth Prediction Method for Low-latency Chunked Streaming

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 16, Issue 2s
      Special Issue on Smart Communications and Networking for Future Video Surveillance and Special Section on Extended MMSYS-NOSSDAV 2019 Best Papers
      April 2020
      291 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3407689
      Issue’s Table of Contents

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 21 June 2020
      • Online AM: 7 May 2020
      • Accepted: 1 March 2020
      • Revised: 1 February 2020
      • Received: 1 December 2019
      Published in tomm Volume 16, Issue 2s

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format .

    View HTML Format