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A survey of data-driven approach on multimedia QoE evaluation

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Abstract

With the development of mobile communication technology and the growth of mobile device, the requirements for user quality of experience (QoE) become higher and higher. Network operators and content providers are interested in QoE evaluation for improving users’ QoE. However, multimedia QoE evaluation faces severe challenges due to the subjective properties of the QoE. In this paper, we provide a survey of the state of the art about applying data-driven approach on QoE evaluation. Firstly, we describe the way to choose factors influencing QoE. Then we investigate and discuss the strengths and shortcomings of existing machine learning algorithms for modeling and predicting users’ QoE. Finally, we describe our research work on how to evaluate QoE in imbalanced dataset.

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Acknowledgements

This work is partly supported by the State Key Development Program of Basic Research of China (2013CB329005), the National Natural Science Foundation of China (Grant Nos. 61401227, 61571240), the Priority Academic Program Development of Jiangsu Higher Education Institutions, the Natural Science Foundation of Jiangsu Province (BK20161517), the Qing Lan Project, the Scientific Research, Foundation of NUPT (NY217022), the Postdoctoral Science Foundation of China (2017M611881).

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Authors and Affiliations

  1. School of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Ruochen Huang, Xin Wei, Liang Zhou, Chaoping Lv, Hao Meng & Jiefeng Jin

  2. National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Xin Wei & Liang Zhou

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  1. Ruochen Huang
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  2. Xin Wei
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Correspondence to Liang Zhou.

Additional information

Ruochen Huang is currently a PhD candidate in Nanjing University of Posts and Telecommunications, China. His research interest is on quality of experience (QoE) of multimedia delivery/distribution.

Xin Wei received his BS degree in Electronic Science and Technology from the Nanjing University of Posts and Telecommunications, China in 2005, and his PhD degree in Information and Communication Engineering from the Southeast University, China in 2009. From 2009 to 2011, he was a postdoctoral researcher with the Department of Information Science and Electronic Engineering, Zhejiang University, China. Now, he was an associate professor with college of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, China. His current research interests include machine learning, pattern recognition, and statistical signal processing.

Liang Zhou is a professor in Nanjing University of Posts and Telecommunications, China. His research interests include multimedia communications and multimedia signal processing.

Chaoping Lv received her BE degree in Nanjing University of Posts and Telecommunications, China in 2016. She will continue to study for a Master’s degree at the same school. Her current research interests include interests include quality of experience (QoE) of multimedia delivery/distribution and machine learning.

Hao Meng received his BE degree of geographic information system from Nanjing University of Posts and Telecommunications, China in 2014. He is currently working towards an MS degree at Nanjing University of Posts and Telecommunications. His research interests include quality of experience (QoE) of multimedia delivery/distribution, machine learning and calculation method of the MOS value for IPTV.

Jiefeng Jin received his BE degree in electronic information engineering in Nanjing Institute of Technology, China in 2014. He is currently working towards an MS degree at Nanjing University of Posts and Telecommunications. His research interests include quality of experience (QoE) of multimedia delivery/distribution and machine learning.

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Huang, R., Wei, X., Zhou, L. et al. A survey of data-driven approach on multimedia QoE evaluation. Front. Comput. Sci. 12, 1060–1075 (2018). https://doi.org/10.1007/s11704-018-6342-7

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