%0 Journal Article
%T Evaluate mobile video quality with LTE radio access network parameters<br>Evaluate mobile video quality with LTE radio access network parameters
%A 王飞
%A 陈亮
%A 邓晓琳
%A 费泽松
%A 韩广林
%A 万蕾
%J 北京理工大学学报（自然科学中文版)
%D 2016
%R 10.15918/j.jbit1004-0579.201625.0415
%X To evaluate the video quality, we tested sample videos delivered using HTTP adaptive streaming (HAS) in LTE network. In order to establish a correlation between radio access network (RAN) performance and quality of experience (QoE), we set up a testbed under different radio impairment conditions with three parameters: signal to interference and noise ratio (SINR), an amount of available network resource and a round trip latency. End users graded each video in a mobile equipment with their QoE Mearnwhile, we used a nonlinear model to simulate the comprehensive predicted mean opinion score (pMOS). Our results show that the nonlinear model can predict the enduser's feedback. The pearson correlation coefficient (PCC) of the model is larger than 0.9. This demonstrate that the output of the model has a high correlation with the end users' ratings and can reflect the QoE accurately. The method we developed will help mobile network operators evaluate the RAN performance of its QoE. It can also be used for HAS service to optimize LTE network and improve its QoE.<br>To evaluate the video quality, we tested sample videos delivered using HTTP adaptive streaming (HAS) in LTE network. In order to establish a correlation between radio access network (RAN) performance and quality of experience (QoE), we set up a testbed under different radio impairment conditions with three parameters: signal to interference and noise ratio (SINR), an amount of available network resource and a round trip latency. End users graded each video in a mobile equipment with their QoE Mearnwhile, we used a nonlinear model to simulate the comprehensive predicted mean opinion score (pMOS). Our results show that the nonlinear model can predict the enduser's feedback. The pearson correlation coefficient (PCC) of the model is larger than 0.9. This demonstrate that the output of the model has a high correlation with the end users' ratings and can reflect the QoE accurately. The method we developed will help mobile network operators evaluate the RAN performance of its QoE. It can also be used for HAS service to optimize LTE network and improve its QoE.
%K quality of experience (QoE) HTTP adaptive streaming (HAS) radio access network (RAN) mobile video&lt
%K br&gt
%K quality of experience (QoE) HTTP adaptive streaming (HAS) radio access network (RAN) mobile video
%U http://journal.bit.edu.cn/yw/bjlgyw/ch/reader/view_abstract.aspx?file_no=20160415&flag=1