More Web Proxy on the site http://driver.im/

research-article

Opportunistic Transmission for Video Streaming over Wild Internet

Authors:

Jianxin WangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 18, Issue 3s

Article No.: 140, Pages 1 - 22

https://doi.org/10.1145/3488722

Published: 11 February 2023 Publication History

Abstract

The video streaming system employs adaptive bitrate (ABR) algorithms to optimize a user’s quality of experience. However, it is hard for ABR algorithms to choose the right bitrate consistently under highly dynamic bandwidth fluctuations in wild Internet. In this article, we propose a building block on the client side named Opportunistic Chunk Replacement Mechanism (OCRM) to help existing ABR algorithms make full use of the available bandwidth to improve the network utilization and viewing experience of users. Specifically, the servers take advantages of the spare bandwidth to opportunistically transmit high-quality chunks (called opportunistic chunks) with low priority to the client, without incurring any extra delay. Then, the client player replaces the low-quality chunks with the opportunistic ones that have high quality. We compare OCRM with state-of-the-art ABR algorithms by using trace-driven experiments spanning a wide variety of quality of experience metrics and network conditions. The test results show that OCRM effectively achieves high network utilization and improves the user’s viewing experience by up to 35%.

References

[1]

GMDT Forecast. 2019. Cisco visual networking index: Global mobile data traffic forecast update, 2017–2022. Update 2017 (2019), 2022.

[2]

ISO/IEC MPEG. 2012. Information Technology—Dynamic Adaptive Streaming over HTTP (DASH)—Part 1: Media Presentation Description and Segment Formats. ISO/IEC MPEG, Technical Report.

[3]

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. ACM SIGCOMM Computer Communication Review 44, 4, (2014), 187–198.

Digital Library

[4]

Kevin Spiteri, Rahul Urgaonkar, and Ramesh K. Sitaraman. 2020. BOLA: Near-optimal bitrate adaptation for online videos. IEEE/ACM Transactions on Networking 28, 4 (2020), 1698–1711.

Digital Library

[5]

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. 44–58.

Digital Library

[6]

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. 197–210.

Digital Library

[7]

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. 272–285.

Digital Library

[8]

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. 325–338.

Digital Library

[9]

Yanyuan Qin, Shuai Hao, Krishna R. Pattipati, Feng Qian, Subhabrata Sen, Bing Wang, and Chaoqun Yue. 2018. ABR streaming of VBR-encoded videos: Characterization, challenges, and solutions. In Proceedings of the 14th International Conference on Emerging Networking Experiments and Technologies. 366–378.

Digital Library

[10]

Junchen Jiang, Vyas Sekar, and Hui Zhang. 2012. Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE. In Proceedings of the 8th International Conference on Emerging Networking Experiments and Technologies. 97–108.

Digital Library

[11]

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 Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI’20). 495–511.

[12]

Akamai. n.d. Dash.js. Retrieved March 20, 2022 from https://github.com/Dash-Industry-Forum/dash.js/.

[13]

P. Halvorsen. n.d. HSDPA Dataset. Retrieved March 20, 2022 from http://home.ifi.uio.no/paalh/dataset/hsdpa-tcp-logs/.

[14]

Bo Wang and Fengyuan Ren. 2017. Towards forward-looking online bitrate adaptation for DASH. In Proceedings of the 2017 ACM International Conference on Multimedia (MM’17). 1122–1129.

Digital Library

[15]

Weihe Li, Jiawei Huang, Shiqi Wang, Sen Liu, and Jianxin Wang. 2020. DAVS: Dynamic-chunk quality aware adaptive video streaming using apprenticeship learning. In Proceedings of the 2020 IEEE GLOBECOM Conference. 1–6.

Digital Library

[16]

Kevin Spiteri, Ramesh Sitaraman, and Daniel Sparacio. 2019. From theory to practice: Improving bitrate adaptation in the DASH reference player. ACM Transactions on Multimedia Computing, Communications, and Applications 15, 2s (2019), 1–29.

Digital Library

[17]

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 (MMSys’20). 321–326.

Digital Library

[18]

J. van der Hooft. n.d. 4G/LTE Bandwidth Logs. Retrieved March 20, 2022 from http://users.ugent.be/jvdrhoof/dataset-4g/.

[19]

Federal Communications Commission. n.d. Measuring Broadband America. Retrieved March 20, 2022 from https://www.fcc.gov/reports-research/reports/measuring-broadband-america.

[20]

Chenghao Liu, Imed Bouazizi, and Moncef Gabbouj. 2011. Rate adaptation for adaptive HTTP streaming. In Proceedings of the 2nd Annual ACM Conference on Multimedia Systems (MMSys’11). 169–174.

Digital Library

[21]

Zhi Li, Xiaoqing Zhu, Joshua Gahm, Rong Pan, Hao Hu, Ali C. Begen, and David Oran. 2014. Probe and adapt: Rate adaptation for HTTP video streaming at scale. IEEE Journal on Selected Areas in Communications 32, 4 (2014), 719–733.

[22]

Tianchi Huang, Chao Zhou, Rui-Xiao Zhang, Chenglei Wu, Xin Yao, and Lifeng Sun. 2019. Comyco: Quality-aware adaptive video streaming via imitation learning. In Proceedings of the 27th ACM International Conference on Multimedia (MM’19). 429–437.

Digital Library

[23]

Yusuf Sani, Andreas Mauthe, and Christopher Edwards. 2017. Adaptive bitrate selection: A survey. IEEE Communications Surveys & Tutorials 19, 4 (2017), 2985–3014.

[24]

Chenghao Liu, Imed Bouazizi, Miska M. Hannuksela, and Moncef Gabbouj. 2012. Rate adaptation for dynamic adaptive streaming over HTTP in content distribution network. Signal Processing: Image Communication 27, 4 (2012), 288–311.

Digital Library

[25]

Bo Han, Feng Qian, Lusheng Ji, and Vijay Gopalakrishnan. 2016. MP-DASH: Adaptive video streaming over preference-aware multipath. In Proceedings of the 12th International on Conference on Emerging Networking Experiments and Technologies. 129–143.

Digital Library

[26]

Jiyan Wu, Chau Yuen, Bo Cheng, Ming Wang, and Junliang Chen. 2015. Streaming high-quality mobile video with multipath TCP in heterogeneous wireless networks. IEEE Transactions on Mobile Computing 15, 9 (2015), 2345–2361.

Digital Library

Cited By

Gayathri SSurendran D(2024)Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networksJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00595-y13:1Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1186/s13677-024-00595-y
Li WHuang JSu QJiang WWang J(2024)VASE: Enhancing Adaptive Bitrate Selection for VBR-Encoded Audio and Video Content With Deep Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.344837023:12(14889-14902)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3448370
Xiang SRong HChen JLiu DJiang H(2024)TSBG: A Two-Stage Stackelberg Game Algorithm for QoE-Awareness Video Streaming TransmissionIEEE Transactions on Mobile Computing10.1109/TMC.2024.341286023:12(12558-12571)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3412860
Show More Cited By

Index Terms

Opportunistic Transmission for Video Streaming over Wild Internet
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

Recommendations

Evaluation of CMAF in live streaming scenarios
NOSSDAV '20: Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video

HTTP Adaptive Streaming (HAS) technologies such as MPEG DASH are now used extensively to deliver television services to large numbers of viewers. In HAS, the client requests segments of content using HTTP, with an ABR algorithm selecting the quality at ...
Low-Latency Live Video Streaming over a Low-Earth-Orbit Satellite Network with DASH
MMSys '24: Proceedings of the 15th ACM Multimedia Systems Conference

In light of Starlink's recent rapid growth in constructing a global low-Earth-orbit satellite constellation and offering high-speed, low-latency Internet services, the implications of utilizing Starlink for low-latency live video streaming, particularly ...
The ACM Multimedia 2019 Live Video Streaming Grand Challenge
MM '19: Proceedings of the 27th ACM International Conference on Multimedia

Live video streaming delivery over Dynamic Adaptive Video Streaming (DASH) is challenging as it requires low end-to-end latency, is more prone to stall, and the receiver has to decide online which representation at which bitrate to download and whether ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 18, Issue 3s

October 2022

381 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3567476

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

Issue’s Table of Contents

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: 11 February 2023

Online AM: 12 March 2022

Accepted: 24 September 2021

Revised: 18 August 2021

Received: 05 April 2021

Published in TOMM Volume 18, Issue 3s

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Natural Science Foundation of China
Key Research and Development Program of Hunan
Natural Science Foundation of Hunan Province, China
China Postdoctoral Science Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
323
Total Downloads

Downloads (Last 12 months)85
Downloads (Last 6 weeks)7

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gayathri SSurendran D(2024)Unified ensemble federated learning with cloud computing for online anomaly detection in energy-efficient wireless sensor networksJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-024-00595-y13:1Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1186/s13677-024-00595-y
Li WHuang JSu QJiang WWang J(2024)VASE: Enhancing Adaptive Bitrate Selection for VBR-Encoded Audio and Video Content With Deep Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.344837023:12(14889-14902)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3448370
Xiang SRong HChen JLiu DJiang H(2024)TSBG: A Two-Stage Stackelberg Game Algorithm for QoE-Awareness Video Streaming TransmissionIEEE Transactions on Mobile Computing10.1109/TMC.2024.341286023:12(12558-12571)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3412860
Li WHuang JLiang YSu QLiu JLyu WWang J(2024)Optimizing Video Streaming in Dynamic Networks: An Intelligent Adaptive Bitrate Solution Considering Scene Intricacy and Data BudgetIEEE Transactions on Mobile Computing10.1109/TMC.2024.340640923:12(12280-12297)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3406409
Nos TElmeligy AHassan MLandolsi TIsmail M(2024)Buffer-Based Adaptive Bitrate Algorithm for Enhanced Quality of Experience2024 International Telecommunications Conference (ITC-Egypt)10.1109/ITC-Egypt61547.2024.10620520(721-726)Online publication date: 22-Jul-2024
https://doi.org/10.1109/ITC-Egypt61547.2024.10620520
Kang JChung K(2024)RL-based HTTP adaptive streaming with edge collaboration in multi-client environmentJournal of Network and Computer Applications10.1016/j.jnca.2024.103833223(103833)Online publication date: Mar-2024
https://doi.org/10.1016/j.jnca.2024.103833
Lee ABae HKim C(2024)COCKTAILComputer Communications10.1016/j.comcom.2024.01.014219:C(204-215)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.comcom.2024.01.014
Li WHuang JSu QJiang WWang J(2024)A learning-based approach for video streaming over fluctuating networks with limited playback buffersComputer Communications10.1016/j.comcom.2023.11.027214:C(113-122)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.comcom.2023.11.027
Xuegang LJunrui LBo WJuan W(2023)Hybrid Low-Rank Tensor CP and Tucker Decomposition with Total Variation Regularization for HSI Noise RemovalWireless Communications & Mobile Computing10.1155/2023/46855632023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/4685563
Wang YWen JZhou WWu QWei YLi HTao B(2022)Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data EnvironmentWireless Communications & Mobile Computing10.1155/2022/19021552022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1902155

View Options

Login options

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

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents