TY - JOUR
T1 - Quality of Experience Oriented Adaptive Video Streaming for Edge Assisted Cellular Networks
AU - Yu, Jun
AU - Wen, Hanfei
AU - Pan, Guangjin
AU - Zhang, Shunqing
AU - Chen, Xiaojing
AU - Xu, Shugong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - HTTP adaptive streaming accounts for a large part of mobile Internet traffic. With the developing cellular communication technologies, the standard dynamic adaptive streaming over HTTP technique allows mobile terminals to adaptively configure the transmission rate of video streaming applications and achieve high quality of experiences (QoE) with many adaptive bitrate algorithms. However, existing schemes either neglect to consider radio access network (RAN) side conditions or only consider simple RAN information optimization schemes. In this letter, we propose a QoE-oriented adaptive video streaming scheme based on a dueling deep Q-learning network. The proposed scheme improves the QoE by jointly considering the physical layer transmission bandwidth and the higher layer buffer status. Through the numerical and prototyping results, we show that our proposed scheme outperforms the existing schemes, with the average QoE improvements of 12.6% to 28.8%.
AB - HTTP adaptive streaming accounts for a large part of mobile Internet traffic. With the developing cellular communication technologies, the standard dynamic adaptive streaming over HTTP technique allows mobile terminals to adaptively configure the transmission rate of video streaming applications and achieve high quality of experiences (QoE) with many adaptive bitrate algorithms. However, existing schemes either neglect to consider radio access network (RAN) side conditions or only consider simple RAN information optimization schemes. In this letter, we propose a QoE-oriented adaptive video streaming scheme based on a dueling deep Q-learning network. The proposed scheme improves the QoE by jointly considering the physical layer transmission bandwidth and the higher layer buffer status. Through the numerical and prototyping results, we show that our proposed scheme outperforms the existing schemes, with the average QoE improvements of 12.6% to 28.8%.
KW - adaptive bitrate
KW - deep Q-learning network
KW - HTTP adaptive streaming
UR - http://www.scopus.com/inward/record.url?scp=85137547424&partnerID=8YFLogxK
U2 - 10.1109/LWC.2022.3200830
DO - 10.1109/LWC.2022.3200830
M3 - Article
AN - SCOPUS:85137547424
SN - 2162-2337
VL - 11
SP - 2305
EP - 2309
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 11
ER -