TY - GEN
T1 - Efficiency of QoE-driven network management in adaptive streaming over HTTP
AU - Tan, Phan Xuan
AU - Kamioka, Eiji
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/10/3
Y1 - 2016/10/3
N2 - HTTP adaptive streaming (HAS) technology has been widely implemented in entertainment industries. It allows users to smoothly access representations of content when the network work conditions frequently fluctuate. This mechanism not only improves the perceived quality of user but also benefits the network resource utilization. However, the frequent adaption of bit rate may cause the instability of Quality of Experience (QoE) to premium users who are willing to pay for high and stable perceived quality. Therefore, recently an appropriate network management scheme has been explored in order to control streaming behaviors with respect to the requirements of various types of users. In our previous study, a machine learning based network management system has been proposed as a relevant approach to manage QoE of HAS. In this paper, the performance of the proposed system will be clarified in dealing with a practical problem of bandwidth competition between a HAS player and other application clients.
AB - HTTP adaptive streaming (HAS) technology has been widely implemented in entertainment industries. It allows users to smoothly access representations of content when the network work conditions frequently fluctuate. This mechanism not only improves the perceived quality of user but also benefits the network resource utilization. However, the frequent adaption of bit rate may cause the instability of Quality of Experience (QoE) to premium users who are willing to pay for high and stable perceived quality. Therefore, recently an appropriate network management scheme has been explored in order to control streaming behaviors with respect to the requirements of various types of users. In our previous study, a machine learning based network management system has been proposed as a relevant approach to manage QoE of HAS. In this paper, the performance of the proposed system will be clarified in dealing with a practical problem of bandwidth competition between a HAS player and other application clients.
KW - HAS
KW - QoE
KW - bandwidth competition
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=84994651570&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84994651570&partnerID=8YFLogxK
U2 - 10.1109/APCC.2016.7581519
DO - 10.1109/APCC.2016.7581519
M3 - Conference contribution
AN - SCOPUS:84994651570
T3 - Proceedings - Asia-Pacific Conference on Communications, APCC 2016
SP - 517
EP - 522
BT - Proceedings - Asia-Pacific Conference on Communications, APCC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd Asia-Pacific Conference on Communications, APCC 2016
Y2 - 25 August 2016 through 27 August 2016
ER -