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DeeProphet: Improving HTTP Adaptive Streaming for Low Latency Live Video by Meticulous Bandwidth Prediction

Authors:

Fengyuan RenAuthors Info & Claims

WWW '23: Proceedings of the ACM Web Conference 2023

Pages 2991 - 3001

https://doi.org/10.1145/3543507.3583364

Published: 30 April 2023 Publication History

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Abstract

The performance of HTTP adaptive streaming (HAS) depends heavily on the prediction of end-to-end network bandwidth. The increasingly popular low latency live streaming (LLLS) faces greater challenges since it requires accurate, short-term bandwidth prediction, compared with VOD streaming which needs long-term bandwidth prediction and has good tolerance against prediction error. Part of the challenges comes from the fact that short-term bandwidth experiences both large abrupt changes and uncertain fluctuations. Additionally, it is hard to obtain valid bandwidth measurement samples in LLLS due to its inter-chunk and intra-chunk sending idleness. In this work, we present DeeProphet, a system for accurate bandwidth prediction in LLLS to improve the performance of HAS. DeeProphet overcomes the above challenges by collecting valid measurement samples using fine-grained TCP state information to identify the packet bursting intervals, and by combining the time series model and learning-based model to predict both large change and uncertain fluctuations. Experiment results show that DeeProphet improves the overall QoE by 17.7%-359.2% compared with state-of-the-art LLLS ABR algorithms, and reduces the median bandwidth prediction error to 2.7%.

References

[1]

2018. Internet-Draft: Delivery Rate Estimation. https://tools.ietf.org/html/draft-cheng-iccrg-delivery-rate-estimation

[2]

2021. Akamai - Improve stream transfer with ultra low latency. https://learn.akamai.com/en-us/webhelp/media-services-live/media-services-live-ingest-users-guide-v4/GUID-DAE55D1E-5EAC-4274-843B-2065B9254668.html

[3]

2021. Best practices for ULL. https://learn.akamai.com/en-us/webhelp/media-services-live/media-services-live-ingest-users-guide-v4/GUID-2406C28A-6DE5-44E6-9A1D-1AD4568D0838.html

[4]

2021. Cisco, Global - 2021 Forecast Highlights. https://www.cisco.com/c/dam/m/en_us/solutions/service-provider/vni-forecast-highlights/pdf/Global_2021_Forecast_Highlights.pdf

[5]

2021. ISO/IEC 23000-19:2020 Information technology — Multimedia application format (MPEG-A) — Part 19: Common media application format (CMAF) for segmented media. https://www.iso.org/standard/79106.html

[6]

2021. ISO/IEC 23009-1:2014 Information technology — Dynamic adaptive streaming over HTTP (DASH). https://www.iso.org/standard/65274.html

[7]

2021. RFC 8216 HTTP live streaming. https://datatracker.ietf.org/doc/html/rfc8216

[8]

2022. Access-Control-Expose-Headers. https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Access-Control-Expose-Headers

[9]

2022. Cisco Visual Networking Index (VNI) Complete Forecast Update,. https://www.cisco.com/c/dam/m/en_us/network-intelligence/service-provider/digital-transformation/knowledge-network-webinars/pdfs/1211_BUSINESS_SERVICES_CKN_PDF.pdf

[10]

2022. DASH Low Latency Streaming. https://github.com/Dash-Industry-Forum/dash.js/wiki/Low-Latency-streaming

[11]

2022. DASH Reference Client. https://reference.dashif.org/dash.js/nightly/samples/dash-if-reference-player/index.html

[12]

2022. Dash.js. https://github.com/Dash-Industry-Forum/dash.js

[13]

2022. FFmpeg:A complete, cross-platform solution to record, convert and stream audio and video. https://ffmpeg.org

[14]

2022. Grand Challenge on Adaptation Algorithms for Near-Second Latency. https://2020.acmmmsys.org/lll_challenge.php

[15]

2022. gRPC. https://grpc.io/

[16]

2022. Linux traffic control. https://man7.org/linux/man-pages/man8/tc.8.html

[17]

2022. Oboe trace. https://github.com/USC-NSL/Oboe

[18]

2022. Puppeteer: Headless Chrome Node.js API. https://github.com/puppeteer/puppeteer

[19]

2022. QoE Evaluation for CMAF-based Low-latency Streaming. https://github.com/twitchtv/acm-mmsys-2020-grand-challenge/blob/master/NQoE.pdf

[20]

2022. Raw Data—Measuring Broadband America. https://www.fcc.gov/reports-research/reports

[21]

2022. Redis. https://redis.io/

[22]

Zahaib Akhtar, Yun Seong Nam, Ramesh Govindan, Sanjay Rao, Jessica Chen, Ethan Katz-Bassett, Bruno Ribeiro, Jibin Zhan, and Hui Zhang. 2018. Oboe: Auto-Tuning Video ABR Algorithms to Network Conditions. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (Budapest, Hungary) (SIGCOMM ’18). Association for Computing Machinery, New York, NY, USA, 44–58. https://doi.org/10.1145/3230543.3230558

Digital Library

[23]

Abdelhak Bentaleb, Mehmet N. Akcay, May Lim, Ali C. Begen, and Roger Zimmermann. 2021. Catching the Moment with LoL+ in Twitch-Like Low-Latency Live Streaming Platforms. IEEE Transactions on Multimedia (2021), 1–1. https://doi.org/10.1109/TMM.2021.3079288

Digital Library

[24]

Gaetano Carlucci, Luca De Cicco, Stefan Holmer, and Saverio Mascolo. 2016. Analysis and Design of the Google Congestion Control for Web Real-Time Communication (WebRTC). In Proceedings of the 7th International Conference on Multimedia Systems (Klagenfurt, Austria) (MMSys ’16). Association for Computing Machinery, New York, NY, USA, Article 13, 12 pages. https://doi.org/10.1145/2910017.2910605

Digital Library

[25]

A. C. Harvey. 1990. ARIMA Models. Palgrave Macmillan UK, London, 22–24. https://doi.org/10.1007/978-1-349-20865-4_2

[26]

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 2014 ACM Conference on SIGCOMM (Chicago, Illinois, USA) (SIGCOMM ’14). Association for Computing Machinery, New York, NY, USA, 187–198. https://doi.org/10.1145/2619239.2626296

Digital Library

[27]

R. Jain and S. Routhier. 1986. Packet Trains–Measurements and a New Model for Computer Network Traffic. IEEE Journal on Selected Areas in Communications 4, 6 (1986), 986–995. https://doi.org/10.1109/JSAC.1986.1146410

Digital Library

[28]

Junchen Jiang, Vyas Sekar, Henry Milner, Davis Shepherd, Ion Stoica, and Hui Zhang. 2016. CFA: A Practical Prediction System for Video QoE Optimization. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16). USENIX Association, Santa Clara, CA, 137–150. https://www.usenix.org/conference/nsdi16/technical-sessions/presentation/jiang

Digital Library

[29]

Junchen Jiang, Shijie Sun, Vyas Sekar, and Hui Zhang. 2017. Pytheas: Enabling Data-Driven Quality of Experience Optimization Using Group-Based Exploration-Exploitation. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17). USENIX Association, Boston, MA, 393–406. https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/jiang

[30]

Theo Karagkioules, Rufael Mekuria, Dirk Griffioen, and Arjen Wagenaar. 2020. Online Learning for Low-Latency Adaptive Streaming. In Proceedings of the 11th ACM Multimedia Systems Conference (Istanbul, Turkey) (MMSys ’20). Association for Computing Machinery, New York, NY, USA, 315–320. https://doi.org/10.1145/3339825.3397042

Digital Library

[31]

May Lim, Mehmet N. Akcay, Abdelhak Bentaleb, Ali C. Begen, and Roger Zimmermann. 2020. When They Go High, We Go Low: Low-Latency Live Streaming in Dash.Js with LoL. In Proceedings of the 11th ACM Multimedia Systems Conference (Istanbul, Turkey) (MMSys ’20). Association for Computing Machinery, New York, NY, USA, 321–326. https://doi.org/10.1145/3339825.3397043

Digital Library

[32]

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 (Los Angeles, CA, USA) (SIGCOMM ’17). Association for Computing Machinery, New York, NY, USA, 197–210. https://doi.org/10.1145/3098822.3098843

Digital Library

[33]

Kevin Spiteri, Rahul Urgaonkar, and Ramesh K. Sitaraman. 2020. BOLA: Near-Optimal Bitrate Adaptation for Online Videos. IEEE/ACM Trans. Netw. 28, 4 (aug 2020), 1698–1711. https://doi.org/10.1109/TNET.2020.2996964

Digital Library

[34]

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 2016 ACM SIGCOMM Conference (Florianopolis, Brazil) (SIGCOMM ’16). Association for Computing Machinery, New York, NY, USA, 272–285. https://doi.org/10.1145/2934872.2934898

Digital Library

[35]

Praveen Kumar Yadav, Abdelhak Bentaleb, May Lim, Junyi Huang, Wei Tsang Ooi, and Roger Zimmermann. 2021. Playing Chunk-Transferred DASH Segments at Low Latency with QLive. In Proceedings of the 12th ACM Multimedia Systems Conference (Istanbul, Turkey) (MMSys ’21). Association for Computing Machinery, New York, NY, USA, 51–64. https://doi.org/10.1145/3458305.3463376

Digital Library

[36]

Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, and Keith Winstein. 2020. Learning in situ: a randomized experiment in video streaming. In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20). USENIX Association, Santa Clara, CA, 495–511. https://www.usenix.org/conference/nsdi20/presentation/yan

[37]

Xiaoqi Yin, Abhishek Jindal, Vyas Sekar, and Bruno Sinopoli. 2015. A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP. In Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication (London, United Kingdom) (SIGCOMM ’15). Association for Computing Machinery, New York, NY, USA, 325–338. https://doi.org/10.1145/2785956.2787486

Digital Library

[38]

Tong Zhang, Fengyuan Ren, Wenxue Cheng, Xiaohui Luo, Ran Shu, and Xiaolan Liu. 2017. Modeling and analyzing the influence of chunk size variation on bitrate adaptation in DASH. In IEEE INFOCOM 2017 - IEEE Conference on Computer Communications. 1–9. https://doi.org/10.1109/INFOCOM.2017.8057057

Cited By

Hou BYang SLi FZhu LJiao LChen XFu X(2024)Gamora: Learning-Based Buffer-Aware Preloading for Adaptive Short Video StreamingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.345656735:11(2132-2146)Online publication date: Nov-2024
https://doi.org/10.1109/TPDS.2024.3456567
Su QHuang JLi WLi ZZhang TJiang WWang J(2024)Achieving QoE Fairness in Video Streaming over Heterogeneous Congestion Control Protocols2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)10.1109/IWQoS61813.2024.10682883(1-10)Online publication date: 19-Jun-2024
https://doi.org/10.1109/IWQoS61813.2024.10682883
Bentaleb AHammoudi S(2024) BML 3 : Accurate Bandwidth Measurement for QoE Optimization in Low Latency Live Streaming 2024 20th International Conference on the Design of Reliable Communication Networks (DRCN)10.1109/DRCN60692.2024.10539140(39-46)Online publication date: 6-May-2024
https://doi.org/10.1109/DRCN60692.2024.10539140

Index Terms

DeeProphet: Improving HTTP Adaptive Streaming for Low Latency Live Video by Meticulous Bandwidth Prediction
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming
2. Networks
  1. Network protocols
    1. Application layer protocols

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Published In

cover image ACM Conferences

WWW '23: Proceedings of the ACM Web Conference 2023

April 2023

4293 pages

ISBN:9781450394161

DOI:10.1145/3543507

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

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Published: 30 April 2023

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WWW '23

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WWW '23: The ACM Web Conference 2023

April 30 - May 4, 2023

TX, Austin, USA

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Cited By

Hou BYang SLi FZhu LJiao LChen XFu X(2024)Gamora: Learning-Based Buffer-Aware Preloading for Adaptive Short Video StreamingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.345656735:11(2132-2146)Online publication date: Nov-2024
https://doi.org/10.1109/TPDS.2024.3456567
Su QHuang JLi WLi ZZhang TJiang WWang J(2024)Achieving QoE Fairness in Video Streaming over Heterogeneous Congestion Control Protocols2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS)10.1109/IWQoS61813.2024.10682883(1-10)Online publication date: 19-Jun-2024
https://doi.org/10.1109/IWQoS61813.2024.10682883
Bentaleb AHammoudi S(2024) BML 3 : Accurate Bandwidth Measurement for QoE Optimization in Low Latency Live Streaming 2024 20th International Conference on the Design of Reliable Communication Networks (DRCN)10.1109/DRCN60692.2024.10539140(39-46)Online publication date: 6-May-2024
https://doi.org/10.1109/DRCN60692.2024.10539140

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