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

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Published:31 July 2018Publication History
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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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    • Published in

      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
      Issue’s Table of Contents

      Copyright © 2018 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 31 July 2018
      • Accepted: 1 April 2018
      • Revised: 1 March 2018
      • Received: 1 September 2017
      Published in tomm Volume 14, Issue 3s

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