Variability-Aware Latency Amelioration in Distributed Environments

Application designers of collaborative distributed virtual environments must account for the influence of the network connection and its detrimental effects on user performance. Based upon analysis and classification of existing latency compensation techniques, this paper introduces a novel approach to latency amelioration 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 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 a state-of-the-art one, which assumes a constant system delay