MOTION-AWARE ADAPTIVE DEAD RECKONING ALGORITHM FOR DISTRIBUTED VIRTUAL REALITY SYSTEMS

Dead reckoning algorithms are employed in distributed virtual reality systems (DVR systems) for predicting objects states at any given moment of time that makes it possible to minimize bandwidth requirements while maintaining required data consistency. Howeven existing implementations often do not take into account information on the object motion dynamics and, in general, apply static prediction models. In this paper a novel motion-aware adaptive dead reckoning algorithm is introduced based on dynamical prediction model selection depending on the object motion pattern. The results show that considerable reduction in update messages can be achieved without sacrificing prediction accuracy. In addition, it becomes possible to dynamically adjust the size of update messages according to the motion pattern and, thus, provide more flexible use of network bandwidth.