UC San Diego

Over the past few years, there has been an increased number of media applications that have migrated to the cloud, which will enable mobile users to engage in rich media experiences from any mobile platform. In this research, we develop techniques to model and enhance the quality of service for two cloud mobile media applications: Cloud Mobile Rendering (CMR) application and HTTP-based adaptive video streaming.

In CMR application, compute intensive graphic rendering is performed on cloud servers, and the rendered video is encoded and transmitted through wireless network to mobile devices. Although promising to address the computation and battery limits of mobile devices, this approach suffers from challenges posed by limited and unstable wireless bandwidth, such that it is difficult to ensure the quality of service. In this research, we first develop a model to quantitatively measure the user experience of CMR application, considering rendering factors, encoding factors and network factors simultaneously. Secondly we derive several techniques to enhance the quality of service, including a)an adaptive rendering technique which dynamically select the optimal rendering factor based on network condition; b) a prioritized encoding technique which allocates bits among different regions of video frame depending on their priorities; c) an encoding acceleration technique which utilizes rendering information to reduce the computation complexity of video encoding while maintaining the video quality.

In HTTP-based adaptive video streaming, video content are split into a sequence of small segments, which are encoded into several versions with different bit rates and quality, and streamed according to the requests from streaming client. The version of video segments will keep varying according to the available bandwidth. Flucating bandwidth will lead to temporal artifacts such as video rebuffering (freezing), and spatial artifacts such as visual quality variation. In this thesis, we develop the first user experience model for this application through extensive subjective experiments. We validate with high accuracy the user experience model, and demonstrates its applicability to video with different length and motion characteristics.