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

Author:

KANAI Kenji1,WEI Bo1,CHENG Zhengxue1,TAKEUCHI Masaru1,KATTO Jiro1

Affiliation:

1. Waseda University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Software

Reference31 articles.

1. [1] “Complete Visual Networking Index (VNI) Forecast,” https://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html

2. [2] I. Sodagar, “The MPEG-DASH standard for multimedia streaming over the Internet,” IEEE Multimedia, vol.18, no.4, pp.62-67, April 2011. 10.1109/mmul.2011.71

3. [3] ISO/IEC 23009-1, “Dynamic adaptive streaming over HTTP (DASH)-Part 1: Media presentation description and segment formats,” 2014. 10.3403/30293350

4. [4] Netflix Tech Blog, “Using Machine Learning to Improve Streaming Quality at Netflix,” https://medium.com/netflix-techblog/using-machine-learning-to-improve-streaming-quality-at-netflix-9651263ef09f, March 2018.

5. [5] K. Kanai, S. Takenaka, J. Katto, and T. Murase, “Energy-efficient mobile video delivery utilizing moving route navigation and video playout buffer control,” IEICE Trans. Commun., vol.E101-B, no.7, pp.1635-1644, July 2018. 10.1587/transcom.2017cqp0010

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