Methods for Adaptive Video Streaming and Picture Quality Assessment to Improve QoS/QoE Performances

Kenji KANAI  Bo WEI  Zhengxue CHENG  Masaru TAKEUCHI  Jiro KATTO  

Publication
IEICE TRANSACTIONS on Communications   Vol.E102-B   No.7   pp.1240-1247
Publication Date: 2019/07/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2018ANI0003
Type of Manuscript: INVITED PAPER (Special Section on Communication Technologies and Service Qualities in Various Access Networks)
Category: 
Keyword: 
video streaming,  picture quality assessment,  MPEG-DASH,  machine learning,  

Full Text: FreePDF(3MB)


Summary: 
This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.