Transmission of video content over wireless access networks (in particular, Wireless Local
Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is
affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing
video quality prediction models.
The main aim of the project is the development of novel and efficient models for video
quality prediction in a non-intrusive way for low bitrate and resolution videos and to
demonstrate their application in QoS-driven adaptation schemes for mobile video streaming
applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length
and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them
and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content
type was found to be the most important parameter.
(3) Efficient regression-based and artificial neural network-based learning models were
developed for video quality prediction over WLAN and UMTS access networks. The
models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and
optimization in network planning and content provisioning for network/service
providers.(4) The applications of the proposed regression-based models were investigated in (i)
optimization of content provisioning and network resource utilization and (ii) A new
fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks.
(5) Finally, Internet-based subjective tests that captured distortions caused by the encoder
and the wireless access network for different types of contents were designed. The
database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.Partially sponsored by EU FP7 ADAMANTIUM Project (EU Contract 214751
