Live 360 Degree Video Delivery Based on User Collaboration in a Streaming Flock

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

Research output: Contribution to journalArticlepeer-review


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. Streaming video falling into user field of view (FoV) can improve bandwidth efficiency of 360-degree video delivery. In this paper, we propose to use the idea of 'flocking' to simultaneously improve the accuracy of user FoV prediction and video delivery efficiency for live 360-degree video streaming. By assigning variable playback latencies to users in a streaming session based on their network conditions, a 'streaming flock' is formed and led by 'strong' users with low playback latencies in the front of the flock. We propose a long short-term memory (LSTM) based collaborative FoV prediction scheme where the FoV traces of users in the front of the flock are utilized to predict the FoV of users behind them. Given a predicted FoV, we develop an optimal rate allocation strategy to maximize the perceptual quality. By conducting experiments using real-world user FoV traces and LTE/5 G network bandwidth traces, we evaluate the gains of the proposed strategies over several benchmarks. Our experimental results demonstrate that the proposed streaming system can increase the overall quality dramatically by about 10 dB compared with heuristic FoV prediction strategy. In addition, the network-aware flocking formation can further reduce the video freeze without influencing video quality.

Original languageEnglish (US)
Pages (from-to)2636-2647
Number of pages12
JournalIEEE Transactions on Multimedia
StatePublished - 2023


  • Multimedia communication
  • machine learning
  • virtual reality

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Computer Science Applications


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