Methods for adaptive video streaming and picture quality assessment to improve QoS/QoE performances

Kenji Kanai, Bo Wei, Zhengxue Cheng, Masaru Takeuchi, Jiro Katto

Research output: Contribution to journalArticle

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)1240-1247
Number of pages8
JournalIEICE Transactions on Communications
Issue number7
Publication statusPublished - 2019 Jan 1



  • Machine learning
  • Picture quality assessment
  • Video streaming

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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