Poster: Adapting XR Perception Serving for Edge Server Scalability

Offloading perception tasks to an edge server enhances extended reality (XR) experiences on resource-constrained mobile devices. However, when serving multiple users, an edge server may face resource contention and not be able to meet the service level objectives (SLOs), leading to degraded user experiences. To improve server scalability, we propose a system that adaptively schedules the inference executions of object detection models based on estimated effectiveness. The perception effectiveness is estimated by the inter-frame similarity and the object distance. We present the initial design of the system and preliminary results demonstrating its feasibility. We describe the next steps to further develop our system.