Quality Prediction-Based Dynamic Content Adaptation Framework Applied to Collaborative Mobile Presentations

Abstract

Today, professional documents, created in applications such as PowerPoint and Word, can be shared using ubiquitous mobile terminals connected to the Internet. GoogleDocs and EasyMeet are good examples of such collaborative web applications dedicated to professional documents. The static adaptation of professional documents has been studied extensively. Dynamic adaptation can be very useful and practical for interactive multimedia applications, because it allows the delivery of highly customized content to the end user without the need to generate and store multiple transcoded versions. In this paper, we propose a dynamic framework that enables us to estimate transcoding parameters on the fly to generate near-optimal adapted content for each user. The framework is compared to current dynamic methods as well as to static adaptation solutions. We show that the proposed framework provides a better tradeoff between quality and storage compared to other static and dynamic approaches. To quantify the quality of the adapted content, we introduce a measure of the quality of the experience based on the visual quality of the adapted content, as well as on the impact of its total delivery time. The framework has been tested on (but is not limited to) OpenOffice Impress presentations.