Link-Level Traffic Modeling of Medical Extended Reality (MXR) Applications

Medical extended reality (MXR) applications are rapidly evolving with innovative use cases in medical training and surgical planning, among others. MXR benefits from seamless connectivity, like that promised by 5G, to facilitate its functionality where users can interact with virtual content, share views, and collaborate. In this paper, we identify practical implementation approaches for MXR connectivity and characterize the traffic models of selected example MXR applications. The traffic models were obtained through a data-oriented workflow to showcase general procedures and considerations in modeling traffic patterns for diverse MXR applications. The proposed models correlate the network traffic with specific MXR deployments (e.g., remote rendering, display mirroring) and events like head rotation, hand movements, and image download. Accordingly, they can be used to formulate the traffic models for other MXR applications to support an application-specific approach to the evaluation of relevant MXR connectivity risks.