TY - GEN
T1 - Flocking-based live streaming of 360-degree video
AU - Sun, Liyang
AU - Mao, Yixiang
AU - Zong, Tongyu
AU - Liu, Yong
AU - Wang, Yao
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/5/27
Y1 - 2020/5/27
N2 - 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.
AB - 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.
KW - collaborative FoV prediction
KW - edge caching
KW - live 360-degree video
UR - http://www.scopus.com/inward/record.url?scp=85086766654&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086766654&partnerID=8YFLogxK
U2 - 10.1145/3339825.3391856
DO - 10.1145/3339825.3391856
M3 - Conference contribution
AN - SCOPUS:85086766654
T3 - MMSys 2020 - Proceedings of the 2020 Multimedia Systems Conference
SP - 26
EP - 37
BT - MMSys 2020 - Proceedings of the 2020 Multimedia Systems Conference
PB - Association for Computing Machinery, Inc
T2 - 11th ACM Multimedia Systems Online Conference, MMSys 2020
Y2 - 8 June 2020 through 11 June 2020
ER -