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A User Driven Adaptive Bandwidth Video Streaming System

사용자 기반 가변 대역폭 영상 스트리밍 시스템

  • Received : 2014.12.30
  • Accepted : 2015.03.12
  • Published : 2015.04.30

Abstract

Adaptive bitrate (ABR) streaming technology has become an important and prevalent feature in many multimedia delivery systems, with content providers such as Netflix and Amazon using ABR streaming to increase bandwidth efficiency and provide the maximum user experience when channel conditions are not ideal. Where such systems could see improvement is in the delivery of live video with a closed loop cognitive control of video encoding. In this paper, we present streaming camera system which provides spatially and temporally adaptive video streams, learning the user's preferences in order to make intelligent scaling decisions. The system employs a hardware based H.264/AVC encoder for video compression. The encoding parameters can be configured by the user or by the cognitive system on behalf of the user when the bandwidth changes. A cognitive video client developed in this study learns the user's preferences(i.e. video size over frame rate) over time and intelligently adapts encoding parameters when the channel conditions change. It has been demonstrated that the cognitive decision system developed has the ability to control video bandwidth by altering the spatial and temporal resolution, as well as the ability to make scaling decisions.

Keywords

Multimedia Communication;Cognitive Computing;Bandwidth Adaptation;Machine Learning;User Preference

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Acknowledgement

Supported by : 원광대학교