Real time video QoE analysis of RTMP streams

  • Authors:

  • Holly French;Jie Lin;Tung Phan;Amy Csizmar Dalal

  • Affiliations:

  • Department of Computer Science, Carleton College, Northfield, MN, USA;Department of Computer Science, Carleton College, Northfield, MN, USA;Department of Computer Science, Carleton College, Northfield, MN, USA;Department of Computer Science, Carleton College, Northfield, MN, USA

  • Venue:
  • PCCC '11 Proceedings of the 30th IEEE International Performance Computing and Communications Conference
  • Year:
  • 2011

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Abstract

We aim to develop self-healing networks that can detect degradation of streaming video quality of experience (QoE), react, and correct the pathology on the network. We present an architecture to assess real time video QoE of RTMP streams. Results from a small set of preliminary experiments demonstrate that we can predict video QoE with 70 -- 80% accuracy based on stream state measurements and previous users' ratings, using at little as 20 seconds of stream state information.