TY - JOUR
T1 - Data analysis on video streaming QoE over mobile networks
AU - Wang, Qingyong
AU - Dai, Hong Ning
AU - Wu, Di
AU - Xiao, Hong
N1 - Funding Information:
The work described in this paper was partially supported by Macao Science and Technology Development Fund under Grant No. 0026/2018/A1, the National Key R&D Program of China under Grant No. 2016YFB0201900, the National Natural Science Foundation of China (NSFC) under Grant No. 61572538 and Grant No. 61672170, the Fundamental Research Funds for the Central Universities under Grant No. 17LGJC23, the NSFC-Guangdong Joint Fund under Grant No. U1401251 and Guangdong Science and Technology Plan with Grant No. 2015B090923004. All the funding bodies are involved with the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Publisher Copyright:
© 2018, The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - One of recent proposals on standardizing quality of user experience (QoE) of video streaming over mobile network is video Mean Opinion Score (vMOS), which can model QoE of video streaming in 5 discrete grades. However, there are few studies on quantifying vMOS and investigating the relationship between vMOS and other quality of service (QoS) parameters. In this paper, we address this concern by proposing a novel data analytical framework based on video streaming QoE data. In particular, our analytical model consists of K-means clustering and logistic regression. This model integrates the benefits of both these two models. Moreover, we conduct extensive experiments on realistic dataset and verify the accuracy of our proposed model. The results show that our proposed framework outperforms other existing methods in terms of prediction accuracy. Moreover, our results also show that vMOS is essentially affected by many QoS parameters such as initial buffering latency, stalling ratio, and stalling times. Our results offer a number of insights in improving QoE of video streaming over mobile networks.
AB - One of recent proposals on standardizing quality of user experience (QoE) of video streaming over mobile network is video Mean Opinion Score (vMOS), which can model QoE of video streaming in 5 discrete grades. However, there are few studies on quantifying vMOS and investigating the relationship between vMOS and other quality of service (QoS) parameters. In this paper, we address this concern by proposing a novel data analytical framework based on video streaming QoE data. In particular, our analytical model consists of K-means clustering and logistic regression. This model integrates the benefits of both these two models. Moreover, we conduct extensive experiments on realistic dataset and verify the accuracy of our proposed model. The results show that our proposed framework outperforms other existing methods in terms of prediction accuracy. Moreover, our results also show that vMOS is essentially affected by many QoS parameters such as initial buffering latency, stalling ratio, and stalling times. Our results offer a number of insights in improving QoE of video streaming over mobile networks.
KW - K-means
KW - Logistics regression
KW - Mobile networks
KW - Quality of experience (QoE)
KW - Quality of service (QoS)
KW - Video streaming
UR - http://www.scopus.com/inward/record.url?scp=85049741849&partnerID=8YFLogxK
U2 - 10.1186/s13638-018-1180-8
DO - 10.1186/s13638-018-1180-8
M3 - Article
AN - SCOPUS:85049741849
SN - 1687-1472
VL - 2018
JO - Eurasip Journal on Wireless Communications and Networking
JF - Eurasip Journal on Wireless Communications and Networking
IS - 1
M1 - 173
ER -