Content classification-based and QoE-driven video send bitrate adaptation scheme

Authors:
Asiya Khan;Lingfen Sun;Emmanuel Jammeh;Emmanuel Ifeachor
Affiliations:
University of Plymouth, Plymouth, UK;University of Plymouth, Plymouth, UK;University of Plymouth, Plymouth, UK;University of Plymouth, Plymouth, UK
Venue:
Proceedings of the 5th International ICST Mobile Multimedia Communications Conference
Year:
2009

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Abstract

Initial video quality requirement is not well understood and content providers usually send video at the highest send bitrate resulting in over-provisioning. The main aim of this paper is to present a new scheme that can adapt video send bitrate according to the dynamics of the content and the user's Quality of Experience (QoE) requirement at the pre-encoding stage. Contents are classified based on their spatio-temporal feature extraction. Video quality is predicted in terms of the Peak-Signal-to-Noise-Ratio (PSNR). Statistical analysis of the experimental results confirms that the proposed adaptation scheme performs well for all content types and hence, improves the perceived end-to-end video quality. The proposed scheme makes it possible for content providers to achieve an optimum streaming scheme (with an appropriate send bitrate) suitable for the content type for a requested QoE.