TY - JOUR
T1 - Discovering Usage Patterns of Mobile Video Service in the Cellular Networks
AU - Yan, Huan
AU - Lin, Tzu Heng
AU - Zeng, Ming
AU - Wu, Jing
AU - Li, Yong
AU - Jin, Depeng
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - With the rapid growth of mobile networks and smart devices, a large number of people prefer to visiting video services via mobile devices. This generates massive data traffic, and thus increases the load of cellular networks. To deal with it, we need to investigate the usage patterns in the video consumption. In this article, we take a data-driven analysis of mobile video services by classifying them into three major types: traditional video portals, user-generated video services and personalized livestreaming. We collect a large dataset of 25,937,758 logs from 455 thousand users, and we find that 1) for the same kind of video services, users exhibit high loyalty; 2) in consecutive days, the traffic peaks have differences among different service types; 3) more video traffic is generated from personalized livestreaming services, and it keeps increasing after midnight at weekdays in the downtown; 4) traffic consumption of user-generated service exhibits great differences under different functional regions; 5) individual users are prone to click within the same service type but possible different time gaps during the consecutive views. Lastly, we utilize these findings to discuss the potential applications in the improvements of cellular networks and video services.
AB - With the rapid growth of mobile networks and smart devices, a large number of people prefer to visiting video services via mobile devices. This generates massive data traffic, and thus increases the load of cellular networks. To deal with it, we need to investigate the usage patterns in the video consumption. In this article, we take a data-driven analysis of mobile video services by classifying them into three major types: traditional video portals, user-generated video services and personalized livestreaming. We collect a large dataset of 25,937,758 logs from 455 thousand users, and we find that 1) for the same kind of video services, users exhibit high loyalty; 2) in consecutive days, the traffic peaks have differences among different service types; 3) more video traffic is generated from personalized livestreaming services, and it keeps increasing after midnight at weekdays in the downtown; 4) traffic consumption of user-generated service exhibits great differences under different functional regions; 5) individual users are prone to click within the same service type but possible different time gaps during the consecutive views. Lastly, we utilize these findings to discuss the potential applications in the improvements of cellular networks and video services.
KW - Mobile video service
KW - cellular network
KW - personalized livestreaming
KW - user-generated video
KW - video portal
UR - https://www.scopus.com/pages/publications/85097956592
U2 - 10.1109/TNSM.2020.3043482
DO - 10.1109/TNSM.2020.3043482
M3 - Article
AN - SCOPUS:85097956592
SN - 1932-4537
VL - 18
SP - 1789
EP - 1802
JO - IEEE Transactions on Network and Service Management
JF - IEEE Transactions on Network and Service Management
IS - 2
M1 - 9288686
ER -