Error resilient interactive video streaming over wireless networks

2007 ◽  
Author(s):  
Marios G. Hadjinicolaou ◽  
Kostas E. Psannis ◽  
Yutaka Ishibashi
Keyword(s):  
Wireless Networks ◽  
Video Streaming ◽  
Interactive Video ◽  
Error Resilient

FEC recovery performance for video streaming services over wired-wireless networks

2009 ◽  
Vol 66 (6) ◽  
pp. 327-342 ◽  
Author(s):  
Shun Muraoka ◽  
Hiroyuki Masuyama ◽  
Shoji Kasahara ◽  
Yutaka Takahashi
Keyword(s):  
Wireless Networks ◽  
Video Streaming ◽  
Streaming Services ◽  
Recovery Performance

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

2019 ◽  
pp. 235-254
Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Video Streaming ◽  
Video Transmission ◽  
Video Quality ◽  
Subjective Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Machine Learning Model

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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