TY - GEN
T1 - Learning from experience
T2 - 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2017
AU - Triki, Imen
AU - El-Azouzi, Rachid
AU - Haddad, Majed
AU - Zhu, Quanyan
AU - Xu, Zhiheng
N1 - Funding Information:
This research is partially supported by NSF grants CNS-1720230, CNS-1544782, and SES-1541164.
Funding Information:
ACKNOWLEDGEMENT This research is partially supported by NSF grants CNS-1720230, CNS-1544782, and SES-1541164.
PY - 2018/2/14
Y1 - 2018/2/14
N2 - The quality of experience (QoE) is known to be subjective and context-dependent. Identifying and calculating the factors that affect QoE is indeed a difficult task. Recently, a lot of effort has been devoted to estimate the users' QoE in order to improve video delivery. In the literature, most of the QoE-driven optimization schemes that realize trade-offs among different quality metrics have been addressed under the assumption of homogenous populations. Nevertheless, people perceptions on a given video quality may not be the same, which makes the QoE optimization a hard task. This paper aims at taking a step further in order to address this limitation and meet users' profiles. Specifically, we propose a closed-loop control framework based on the users' (subjective) feedbacks to learn the QoE function and optimize it at the same time. Extensive simulation results show that the proposed scheme converges to a steady state, where the resulting QoE function noticeably improves the users' feedbacks.
AB - The quality of experience (QoE) is known to be subjective and context-dependent. Identifying and calculating the factors that affect QoE is indeed a difficult task. Recently, a lot of effort has been devoted to estimate the users' QoE in order to improve video delivery. In the literature, most of the QoE-driven optimization schemes that realize trade-offs among different quality metrics have been addressed under the assumption of homogenous populations. Nevertheless, people perceptions on a given video quality may not be the same, which makes the QoE optimization a hard task. This paper aims at taking a step further in order to address this limitation and meet users' profiles. Specifically, we propose a closed-loop control framework based on the users' (subjective) feedbacks to learn the QoE function and optimize it at the same time. Extensive simulation results show that the proposed scheme converges to a steady state, where the resulting QoE function noticeably improves the users' feedbacks.
KW - Average video quality
KW - Learning
KW - Neural network
KW - QoE
KW - Rebuffering delay
KW - Startup delay
KW - Video quality switching
KW - Video stalls
UR - http://www.scopus.com/inward/record.url?scp=85045287947&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045287947&partnerID=8YFLogxK
U2 - 10.1109/PIMRC.2017.8292500
DO - 10.1109/PIMRC.2017.8292500
M3 - Conference contribution
AN - SCOPUS:85045287947
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
SP - 1
EP - 5
BT - 2017 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 October 2017 through 13 October 2017
ER -