research-article

Characterizing User Behaviors in Mobile Personal Livecast: Towards an Edge Computing-assisted Paradigm

Authors:

Jiangchuan Liu,

Kaiyan ChuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 14, Issue 3s

Article No.: 66, Pages 1 - 24

https://doi.org/10.1145/3219751

Published: 31 July 2018 Publication History

Abstract

Mobile personal livecast (MPL) services are emerging and have received great attention recently. In MPL, numerous and geo-distributed ordinary people broadcast their video contents to worldwide viewers. Different from conventional social networking services like Twitter and Facebook, which have a tolerance for interaction delay, the interactions (e.g., chat messages) in a personal livecast must be in real-time with low feedback latency. These unique characteristics inspire us to: (1) investigate how the relationships (e.g., social links and geo-locations) between viewers and broadcasters influence the user behaviors, which has yet to be explored in depth; and (2) explore insights to benefit the improvement of system performance. In this article, we carry out extensive measurements of a representative MPL system, with a large-scale dataset containing 11M users. In the current costly and limited cloud-based MPL system, which is faced with scalability problem, we find: (1) the long content uploading distances between broadcasters and cloud ingesting servers result in an impaired system QoS, including a high broadcast latency and a frequently buffering events; and (2) most of the broadcasters in MPL are geographically locally popular (the majority of the views come from the same region of the broadcaster), which consume vast computation and bandwidth resources of the clouds and Content Delivery Networks. Fortunately, the emergence of edge computing, which provides cloud-computing capabilities at the edge of the mobile network, naturally sheds new light on the MPL system; i.e., localized ingesting, transcoding, and delivering locally popular live content is possible. Based on these critical observations, we propose an edge-assisted MPL system that collaboratively utilizes the core-cloud and abundant edge computing resources to improve the system efficiency and scalability. In our framework, we consider a dynamic broadcaster assignment to minimize the broadcast latency while keeping the resource lease cost low. We formulate the broadcaster scheduling as a stable matching with migration problem to solve it effectively. Compared with the current pure cloud-based system, our edge-assisted delivery approach reduces the broadcast latency by about 35%.

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Cited By

Zhang RYang CWang XHuang TWu CLiu JSun L(2023)Practical Cloud-Edge Scheduling for Large-Scale Crowdsourced Live StreamingIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.326773134:7(2055-2071)Online publication date: 1-Jul-2023
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Zhu GGu W(2022)User Mapping Strategy in Multi-CDN Streaming: A Data-Driven ApproachIEEE Internet of Things Journal10.1109/JIOT.2021.31125619:9(6638-6649)Online publication date: 1-May-2022
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Index Terms

Characterizing User Behaviors in Mobile Personal Livecast: Towards an Edge Computing-assisted Paradigm
1. Information systems
  1. Information systems applications
    1. Multimedia information systems
      1. Multimedia streaming

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

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 14, Issue 3s

Special Section on Delay-Sensitive Video Computing in the Cloud and Special Section on Extended MMSys-NOSSDAV Best Papers

June 2018

317 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3233173

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2018 ACM.

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Publication History

Published: 31 July 2018

Accepted: 01 April 2018

Revised: 01 March 2018

Received: 01 September 2017

Published in TOMM Volume 14, Issue 3s

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Tsinghua-Alibaba Cooperation Project
National Key Research and Development Program of China
National Natural Science Foundation of China
Beijing Key Laboratory of Networked Multimedia

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https://dl.acm.org/doi/10.1109/TPDS.2023.3267731
Wu XZhang LCui LMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)QoE-aware Download Control and Bitrate Adaptation for Short Video StreamingProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3551590(7115-7119)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3551590
Zhu GGu W(2022)User Mapping Strategy in Multi-CDN Streaming: A Data-Driven ApproachIEEE Internet of Things Journal10.1109/JIOT.2021.31125619:9(6638-6649)Online publication date: 1-May-2022
https://doi.org/10.1109/JIOT.2021.3112561
Yan HFu HLi YLin TWang GZheng HJin DZhao B(2021)On Migratory Behavior in Video ConsumptionIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304346718:2(1775-1788)Online publication date: Jun-2021
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