Session management of correlated multi-stream 3D tele-immersive environments

• Authors: Ahsan Arefin
• Venue: MM '11 Proceedings of the 19th ACM international conference on Multimedia
• Year: 2011

Quality control and resource optimization are challenging problems in 3D tele-immersive (3DTI) environments due to their large scale, multi-stream dependencies and dynamic peer (viewer) behavior. Such systems are also prone to performance degradation due to undesired behavior in the event of drastic demand changes, such as view change and large-scale simultaneous viewer arrivals or departures. Therefore, it is crucial to localize undesired behavior inside the system and re-organize the streaming overlay structures accordingly. Doing this accurately for a large scale is even more challenging and it requires to capture all events effecting the data plan and control plan of the system. Moreover, to do this, we need to understand the desired behavior of the application first, which is defined by the dependency patterns of performance and configuration metadata at each participating peers. To assist that, we propose a learning framework that discovers metadata dependency patterns from the time series metadata and uses an online profiler to detect undesired behavior of the system during run-time. Such universal protocol also enables the prediction of large scale performance degradation due to irregular dependencies. Finally an adaptation is proposed that reallocates the resources and rearranges overlay structures to overcome the undesired behavior. In summary, our goal is to provide a universal session monitoring and management framework for complex multi-stream 3DTI environments to support large number of concurrent viewers. We consider the difficulty in overlay construction, collecting metadata, answering queries, learning patterns, detecting undesired behavior at the participating peers and finally overlay adaptation considering multi-stream dependencies.