Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

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Author(s): Monalisa Ghosh (Indian Institute of Technology Kharagpur, India)and Chetna Singhal (Indian Institute of Technology Kharagpur, India)
Copyright: 2019
Pages: 20
Source title: Next-Generation Wireless Networks Meet Advanced Machine Learning Applications
Source Author(s)/Editor(s): Ioan-Sorin Comşa (Brunel University London, UK)and Ramona Trestian (Middlesex University, UK)
DOI: 10.4018/978-1-5225-7458-3.ch010

Keywords: Information Science Reference / Media & Communications / Networking and Telecommunications / Wireless Systems

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Abstract

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.

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Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

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