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Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming


Published In: ACM Multimedia Systems Conference (MMSys) Doctoral Symposium 2021, September 28 - October 01, Istanbul, Turkey - PaperLink

Authors: Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: Video traffic comprises the majority of today’s Internet traffic, and HTTP Adaptive Streaming (HAS) is the preferred method to deliver video content over the Internet. Increasing demand for video and the improvements in the video display conditions over the years caused an increase in the video coding complexity. This increased complexity brought the need for more efficient video streaming and coding solutions. The latest standard video codecs can reduce the size of the videos by using more efficient tools with higher time-complexities. The plans for integrating machine learning into upcoming video codecs raised the interest in applied machine learning for video coding. In this doctoral study, we aim to propose applied machine learning methods to video coding, focusing on HTTP adaptive streaming. We present four primary research questions to target different challenges in video coding for HTTP adaptive streaming.

Keywords: HTTP Adaptive Streaming, Machine Learning, Video Encoding

	author = {\c{C}etinkaya, Ekrem},
	title = {Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming},
	year = {2021},
	isbn = {9781450384346},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {},
	doi = {10.1145/3458305.3478468},
	booktitle = {Proceedings of the 12th ACM Multimedia Systems Conference},
	pages = {418–422},
	numpages = {5},
	keywords = {neural networks, video coding, HAS, machine learning},
	location = {Istanbul, Turkey},
	series = {MMSys '21}