Variability-aware latency amelioration in distributed interactive virtual environments

Designers of applications of collaborative distributed Virtual Environments must account for the impairment of the network connecting them and its detrimental effects on user performance. Based upon analysis and classification of existing latency compensation techniques, this thesis introduces a novel amelioration method in the form of a two-tier predictor-estimator framework. The technique is variability-aware due to its proactive sender-side prediction of a pose a variable time into the future. The prediction interval required is estimated online based on current and past network delay characteristics. This latency estimate is subsequently used by a Kalman Filter-based predictor to replace the measurement event with a predicted pose that matches the event's arrival time at the receiving workstation. The compensation technique was evaluated in a simulation through an offline playback of real head motion data and network delay traces collected under a variety of real network conditions. The experimental results indicate that the variability-aware approach significantly outperforms the one based on state-of-the-art assumption of a constant system delay.