research-article

Cloud-Assisted Crowdsourced Livecast

Authors:

Jiangchuan Liu,

Haiyang WangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 13, Issue 3s

Article No.: 46, Pages 1 - 22

https://doi.org/10.1145/3095755

Published: 14 July 2017 Publication History

Abstract

The past two years have witnessed an explosion of a new generation of livecast services, represented by Twitch.tv, GamingLive, and Dailymotion, to name but a few. With such a livecast service, geo-distributed Internet users can broadcast any event in real-time, for example, game, cooking, drawing, and so on, to viewers of interest. Its crowdsourced nature enables rich interactions among broadcasters and viewers but also introduces great challenges to accommodate their great scales and dynamics. To fulfill the demands from a large number of heterogeneous broadcasters and geo-distributed viewers, expensive server clusters have been deployed to ingest and transcode live streams. Yet our Twitch-based measurement shows that a significant portion of the unpopular and dynamic broadcasters are consuming considerable system resources; in particular, 25% of bandwidth resources and 30% of computational capacity are used by the broadcasters who do not have any viewers at all. In this article, through the real-world measurement and data analysis, we show that the public cloud has great potentials to address these scalability challenges. We accordingly present the design of Cloud-assisted Crowdsourced Livecast (CACL) and propose a comprehensive set of solutions for broadcaster partitioning. Our trace-driven evaluations show that our CACL design can smartly assign ingesting and transcoding tasks to the elastic cloud virtual machines, providing flexible and cost-effective system deployment.

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Cited By

Lee DKim YSong M(2023)Cost-Effective, Quality-Oriented Transcoding of Live-Streamed Video on Edge-ServersIEEE Transactions on Services Computing10.1109/TSC.2023.325642516:4(2503-2516)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2023.3256425
Liu XDerakhshani MMihaylova LLambotharan S(2023)Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live StreamingIEEE Journal on Selected Areas in Communications10.1109/JSAC.2022.322942341:3(740-754)Online publication date: Mar-2023
https://doi.org/10.1109/JSAC.2022.3229423
Kim JKim SBak CHan S(2023)Adaptive cloud resource allocation for large-scale crowdsourced multimedia live streaming servicesCluster Computing10.1007/s10586-023-04138-z27:3(3233-3257)Online publication date: 25-Sep-2023
https://doi.org/10.1007/s10586-023-04138-z
Show More Cited By

Index Terms

Cloud-Assisted Crowdsourced Livecast
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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Published In

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

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 13, Issue 3s

Special Section on Deep Learning for Mobile Multimedia and Special Section on Best Papers from ACM MMSys/NOSSDAV 2016

August 2017

258 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3119899

Editor:
Alberto Del Bimbo
University of Firenze, Italy

Issue’s Table of Contents

Copyright © 2017 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: 14 July 2017

Accepted: 01 March 2017

Received: 01 September 2016

Revised: 01 February 2016

Published in TOMM Volume 13, Issue 3s

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Research-article
Research
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Funding Sources

Chancellor’s Small Grant and Grant-in-aid programs from the University of Minnesota
Qatar National Research Fund (a member of Qatar Foundation)
NPRP
Natural Sciences and Engineering Research Natural Sciences and Engineering Research Council (NSERC) of Canada

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Citations

Cited By

Lee DKim YSong M(2023)Cost-Effective, Quality-Oriented Transcoding of Live-Streamed Video on Edge-ServersIEEE Transactions on Services Computing10.1109/TSC.2023.325642516:4(2503-2516)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2023.3256425
Liu XDerakhshani MMihaylova LLambotharan S(2023)Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live StreamingIEEE Journal on Selected Areas in Communications10.1109/JSAC.2022.322942341:3(740-754)Online publication date: Mar-2023
https://doi.org/10.1109/JSAC.2022.3229423
Kim JKim SBak CHan S(2023)Adaptive cloud resource allocation for large-scale crowdsourced multimedia live streaming servicesCluster Computing10.1007/s10586-023-04138-z27:3(3233-3257)Online publication date: 25-Sep-2023
https://doi.org/10.1007/s10586-023-04138-z
Cabeza-Ramírez LFuentes-García FMuñoz-Fernandez G(2021)Exploring the Emerging Domain of Research on Video Game Live Streaming in Web of Science: State of the Art, Changes and TrendsInternational Journal of Environmental Research and Public Health10.3390/ijerph1806291718:6(2917)Online publication date: 12-Mar-2021
https://doi.org/10.3390/ijerph18062917
Zheng JZhang HJin YMa H(2021)Collaborative Framework of Cloud Transcoding and Distribution Supporting Cost-Efficient Crowdsourced Live Streaming2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS53394.2021.00122(931-938)Online publication date: Dec-2021
https://doi.org/10.1109/ICPADS53394.2021.00122
Rao YWang WShen XJia L(2021)An Analysis of the Streamer Behaviors in Social Live Video StreamingComputer Supported Cooperative Work and Social Computing10.1007/978-981-16-2540-4_43(594-604)Online publication date: 7-May-2021
https://doi.org/10.1007/978-981-16-2540-4_43
Zhuang YXu DYan XCheng WZhao ZPu SXiao J(2020)Multichannel Attention Refinement for Video Question AnsweringACM Transactions on Multimedia Computing, Communications, and Applications10.1145/336671016:1s(1-23)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1145/3366710
Lu Jia ARao YLi HTian RShen S(2020)Revealing Donation Dynamics in Social Live Video StreamingCompanion Proceedings of the Web Conference 202010.1145/3366424.3382682(30-31)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366424.3382682
Baccour EErbad AMohamed AHaouari FGuizani MHamdi M(2020)RL-OPRA: Reinforcement Learning for Online and Proactive Resource Allocation of crowdsourced live videosFuture Generation Computer Systems10.1016/j.future.2020.06.038Online publication date: Jun-2020
https://doi.org/10.1016/j.future.2020.06.038
Li YPan YYao TChao HRui YMei T(2019)Learning Click-Based Deep Structure-Preserving Embeddings with Visual AttentionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/332899415:3(1-19)Online publication date: 8-Aug-2019
https://dl.acm.org/doi/10.1145/3328994
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