QoE Aware Video Content Adaptation and Delivery

The explosion of traffic associated with video content poses significant challenges for mobile content provision. While, on the one hand, mobile video traffic surge is forecast-ed to require significant investments in bandwidth acquisition and infrastructure dimensioning and roll-out, on the other hand, users are not likely to be willing to pay significantly more than today. This increases the pressure to develop solutions capable of making the mobile provision of video more affordable without either affecting user experience or limiting usage. In this respect, this paper proposes a novel methodology for video content delivery which is based on a user video quality perception model. According to this scheme, the video quality of each scene in a movie is selected, from among a finite set of available qualities, with the purpose of reducing the overall bandwidth required to attain a given user experience level targeted by the system for each user and each video. This novel methodology also adopts a clustering approach to identify users with similar Quality of Experience (QoE) profiles and leverages this information for improving the accuracy of user perceived quality predictions. This approach has been validated through a crowd-sourced subjective test evaluation performed with real users using a novel method involving the Amazon Mechanical Turk platform. The results showed that the proposed method is capable of achieving a prediction accuracy in the order of +/- 0.5 MOS points. This approach can be effectively used to select the video qualities minimizing bandwidth costs while delivering predefined level of perceived quality to the end users.