TY - GEN
T1 - On distribution of user movie watching time in a large-scale video streaming system
AU - Chen, Yishuai
AU - Liu, Yong
AU - Zhang, Baoxian
AU - Zhu, Wei
PY - 2014
Y1 - 2014
N2 - Video watching time is a crucial measure for studying user watching behavior in online Internet video-on-demand (VoD) systems. It is important for system planning, user engagement study, and service quality evaluation. However, due to limited access to large-scale VoD systems, there is still a lack of accurate model for characterizing the distribution of user watching time on a per video basis. In this paper, we measure PPLive, one of the most popular commercial Internet VoD systems in China, over a three week period, and characterize user watching time distributions of 1,000 most popular movies. We find that a video's watching time can be modeled by a concatenation of exponential distribution (in the first several minutes of the video) and truncated power law distribution (in the remaining time of the video), when users watch the video without interruptions. For comparison, user watching time with user interactions such as seeking and/or pause operations does not follow such a distribution. We further reveal interesting characteristics regarding the relation between video's watching time distribution and various watching/video-related features (including time-of-day, user ratings, and movie genres). Our measurement and modeling results bring forth important insights for design, deployment, and evaluation of Internet VoD systems.
AB - Video watching time is a crucial measure for studying user watching behavior in online Internet video-on-demand (VoD) systems. It is important for system planning, user engagement study, and service quality evaluation. However, due to limited access to large-scale VoD systems, there is still a lack of accurate model for characterizing the distribution of user watching time on a per video basis. In this paper, we measure PPLive, one of the most popular commercial Internet VoD systems in China, over a three week period, and characterize user watching time distributions of 1,000 most popular movies. We find that a video's watching time can be modeled by a concatenation of exponential distribution (in the first several minutes of the video) and truncated power law distribution (in the remaining time of the video), when users watch the video without interruptions. For comparison, user watching time with user interactions such as seeking and/or pause operations does not follow such a distribution. We further reveal interesting characteristics regarding the relation between video's watching time distribution and various watching/video-related features (including time-of-day, user ratings, and movie genres). Our measurement and modeling results bring forth important insights for design, deployment, and evaluation of Internet VoD systems.
KW - online video
KW - user watching time
KW - video-on-demand
UR - http://www.scopus.com/inward/record.url?scp=84906995491&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84906995491&partnerID=8YFLogxK
U2 - 10.1109/ICC.2014.6883588
DO - 10.1109/ICC.2014.6883588
M3 - Conference contribution
AN - SCOPUS:84906995491
SN - 9781479920037
T3 - 2014 IEEE International Conference on Communications, ICC 2014
SP - 1825
EP - 1830
BT - 2014 IEEE International Conference on Communications, ICC 2014
PB - IEEE Computer Society
T2 - 2014 1st IEEE International Conference on Communications, ICC 2014
Y2 - 10 June 2014 through 14 June 2014
ER -