TY - GEN
T1 - LiveClip
T2 - 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2020, Part of MMSys 2020
AU - He, Jianchao
AU - Hu, Miao
AU - Zhou, Yipeng
AU - Wu, Di
PY - 2020
Y1 - 2020
N2 - Recent years have witnessed great success of mobile short-form video apps. However, most current video streaming strategies are designed for long-form videos, which cannot be directly applied to short-form videos. Especially, short-form videos differ in many aspects, such as shorter video length, mobile friendliness, sharp popularity dynamics, and so on. Facing these challenges, in this paper, we perform an in-depth measurement study on Douyin, one of the most popular mobile short-form video platforms in China. The measurement study reveals that Douyin adopts a rather simple strategy (called Next-One strategy) based on HTTP progressive download, which uses a sliding window with stop-and-wait protocol. Such a strategy performs poorly when network connection is slow and user scrolling is fast. The results motivate us to design an intelligent adaptive streaming scheme for mobile short-form videos. We formulate the short-form video streaming problem and propose an adaptive short-form video streaming strategy called LiveClip using a deep reinforcement learning (DRL) approach. Trace-driven experimental results prove that LiveClip outperforms existing state-of-the-art approaches by around 10%-40% under various scenarios.
AB - Recent years have witnessed great success of mobile short-form video apps. However, most current video streaming strategies are designed for long-form videos, which cannot be directly applied to short-form videos. Especially, short-form videos differ in many aspects, such as shorter video length, mobile friendliness, sharp popularity dynamics, and so on. Facing these challenges, in this paper, we perform an in-depth measurement study on Douyin, one of the most popular mobile short-form video platforms in China. The measurement study reveals that Douyin adopts a rather simple strategy (called Next-One strategy) based on HTTP progressive download, which uses a sliding window with stop-and-wait protocol. Such a strategy performs poorly when network connection is slow and user scrolling is fast. The results motivate us to design an intelligent adaptive streaming scheme for mobile short-form videos. We formulate the short-form video streaming problem and propose an adaptive short-form video streaming strategy called LiveClip using a deep reinforcement learning (DRL) approach. Trace-driven experimental results prove that LiveClip outperforms existing state-of-the-art approaches by around 10%-40% under various scenarios.
KW - Measurements
KW - Reinforcement learning
KW - Short-form video
UR - http://www.scopus.com/inward/record.url?scp=85086563328&partnerID=8YFLogxK
U2 - 10.1145/3386290.3396937
DO - 10.1145/3386290.3396937
M3 - Conference proceeding contribution
AN - SCOPUS:85086563328
T3 - NOSSDAV 2020 - Proceedings of the 2020 Workshop on Network and Operating System Support for Digital Audio and Video, Part of MMSys 2020
SP - 54
EP - 59
BT - NOSSDAV 2020 - Proceedings of the 2020 Workshop on Network and Operating System Support for Digital Audio and Video, Part of MMSys 2020
PB - Association for Computing Machinery (ACM)
CY - New York, NY
Y2 - 10 June 2020 through 11 June 2020
ER -