Predictability-Aware Motion Prediction for Edge XR via High-Order Error-State Kalman Filtering

Abstract

As 6G networks are developed and defined, offloading of XR applications is emerging as one of the strong new use cases. The reduced 6G latency coupled with edge processing infrastructure will for the first time provide a realistic offloading scenario in cellular networks where several computationally intensive functions, including rendering, can migrate from the user device and into the network. A key advantage of doing so is the lowering of the battery needs in the user devices and the possibility to design new devices with smaller form factors. However, offloading introduces increased delays compared to local execution, primarily due to network transmission latency and queuing delays at edge servers, especially under multi-user concurrency. Despite the computational power of edge platforms, the resulting motion-to-photon (MTP) latency negatively impacts user experience. To mitigate this, motion prediction has been proposed to offset delays. Existing approaches build on either deep learning or Kalman filtering. Deep learning techniques face scalability limitations at the resource-constrained edge, as their computational expense intensifies with increasing user concurrency, while Kalman filtering suffers from poor handling of complex movements and fragility to packet loss inherent in 6G's high-frequency radio interfaces. In this work, we introduce a context-aware error-state Kalman filter (ESKF) prediction framework, which forecasts the user's head motion trajectory to compensate for MTP latency in remote XR. By integrating a motion classifier that categorizes head motions based on their predictability, our algorithm demonstrates reduced prediction error across different motion classes. Our findings demonstrate that the optimized ESKF not only surpasses traditional Kalman filters in positional and orientational accuracy but also exhibits enhanced robustness and resilience to packet loss.