Flocking-based live streaming of 360-degree video

Liyang Sun, Yixiang Mao, Tongyu Zong, Yong Liu, Yao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Streaming of live 360-degree video allows users to follow a live event from any view point and has already been deployed on some commercial platforms. However, the current systems can only stream the video at relatively low-quality because the entire 360-degree video is delivered to the users under limited bandwidth. In this paper, we propose to use the idea of "flocking" to improve the performance of both prediction of field of view (FoV) and caching on the edge servers for live 360-degree video streaming. By assigning variable playback latencies to all the users in a streaming session, a "streaming flock" is formed and led by low latency users in the front of the flock. We propose a collaborative FoV prediction scheme where the actual FoV information of users in the front of the flock are utilized to predict of users behind them. We further propose a network condition aware flocking strategy to reduce the video freeze and increase the chance for collaborative FoV prediction on all users. Flocking also facilitates caching as video tiles downloaded by the front users can be cached by an edge server to serve the users at the back of the flock, thereby reducing the traffic in the core network. We propose a latency-FoV based caching strategy and investigate the potential gain of applying transcoding on the edge server. We conduct experiments using real-world user FoV traces and WiGig network bandwidth traces to evaluate the gains of the proposed strategies over benchmarks. Our experimental results demonstrate that the proposed streaming system can roughly double the effective video rate, which is the video rate inside a user's actual FoV, compared to the prediction only based on the user's own past FoV trajectory, while reducing video freeze. Furthermore, edge caching can reduce the traffic in the core network by about 80%, which can be increased to 90% with transcoding on edge server.

Original languageEnglish (US)
Title of host publicationMMSys 2020 - Proceedings of the 2020 Multimedia Systems Conference
PublisherAssociation for Computing Machinery, Inc
Pages26-37
Number of pages12
ISBN (Electronic)9781450368452
DOIs
StatePublished - May 27 2020
Event11th ACM Multimedia Systems Online Conference, MMSys 2020 - Istanbul, Turkey
Duration: Jun 8 2020Jun 11 2020

Publication series

NameMMSys 2020 - Proceedings of the 2020 Multimedia Systems Conference

Conference

Conference11th ACM Multimedia Systems Online Conference, MMSys 2020
CountryTurkey
CityIstanbul
Period6/8/206/11/20

Keywords

  • collaborative FoV prediction
  • edge caching
  • live 360-degree video

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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