TY - JOUR
T1 - Characterizing the Usage of Mobile Video Service in Cellular Networks
AU - Yan, Huan
AU - Lin, Tzu Heng
AU - Zeng, Ming
AU - Wu, Jing
AU - Huang, Jiaxin
AU - Li, Yong
AU - Jin, Depeng
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017
Y1 - 2017
N2 - Due to the proliferation of mobile networks and services, accessing online video services via mobile devices becomes increasingly popular, which generates huge data traffic and increases the cellular network load. It is valuable to characterize the usage patterns of different video services for network optimization and user experience enhancement. In this paper, we adopt a data-driven approach to investigate the usage patterns of three newly popular and major kinds of mobile video services: personalized livestreaming, user-generated video service, and traditional video portals. Based on the empirical analysis of a large dataset including 0.45 million users and 25 million logs, we find that 1) users have high loyalty in the same type of video services; 2) difference on the traffic peaks in consecutive days exists among different kinds of services; 3) personalized livestreaming services contributes a larger portion of video traffic, which still increases after midnight at weekday in the downtown. Finally, based on these findings, we discuss their implications and insights for network optimization and video service enhancement.
AB - Due to the proliferation of mobile networks and services, accessing online video services via mobile devices becomes increasingly popular, which generates huge data traffic and increases the cellular network load. It is valuable to characterize the usage patterns of different video services for network optimization and user experience enhancement. In this paper, we adopt a data-driven approach to investigate the usage patterns of three newly popular and major kinds of mobile video services: personalized livestreaming, user-generated video service, and traditional video portals. Based on the empirical analysis of a large dataset including 0.45 million users and 25 million logs, we find that 1) users have high loyalty in the same type of video services; 2) difference on the traffic peaks in consecutive days exists among different kinds of services; 3) personalized livestreaming services contributes a larger portion of video traffic, which still increases after midnight at weekday in the downtown. Finally, based on these findings, we discuss their implications and insights for network optimization and video service enhancement.
UR - http://www.scopus.com/inward/record.url?scp=85046403465&partnerID=8YFLogxK
U2 - 10.1109/GLOCOM.2017.8254136
DO - 10.1109/GLOCOM.2017.8254136
M3 - Conference article
AN - SCOPUS:85046403465
SN - 2334-0983
VL - 2018-January
SP - 1
EP - 6
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
T2 - 2017 IEEE Global Communications Conference, GLOBECOM 2017
Y2 - 4 December 2017 through 8 December 2017
ER -