A Pattern-based Evaluation of Download and Cache Management Algorithms for Annotated Interactive Non-linear Videos

Abstract

With modern technologies, it is possible to create annotated interactive non-linear videos (a form of hypervideo) for the Web. These videos have a non-linear structure of linked scenes to which additional information (other media like images, text, audio, or additional videos) can be added. A variety of user interactions – like in- and between-scene navigation or zooming into additional information – are possible in players for this type of video. Like linear video, quality of experience (QoE) with annotated hypervideo experiences is tied to the temporal consistency of the video stream at the client end – its flow. Despite its interactive complexity, users expect this type of video experience to flow as seamlessly as simple linear video. However, the added hypermedia elements bog playback engines down.

Download and cache management systems address the flow issue, but their effectiveness is tied to numerous questions respecting user requirements, computational strategy, and evaluative metrics. In this work, we a) define QoE metrics, b) examine structural and behavioral patterns of interactive annotated non-linear video, c) propose download and cache management algorithms and strategies, d) describe the implementation of an evaluative simulation framework, and e) present the algorithm test results.