Optimal Resource Allocation for the Transport of Multi-Modal Visual-Haptic Metaverse Flows in 5G

We study resource allocation for a metaverse user in 5G networks and beyond. To ensure an immersive experience, one should consider the multi-modality nature of the metaverse, where each user generates multiple coupled flows, namely visual and haptic, characterized by joint Quality of Service (QoS) requirements, for instance in terms of subjective Just Noticeable Difference (JND) metric. These flows can be transported via various 5G services, such as Ultra Reliable Low Latency Communications (URLLC) for the haptic flow and enhanced Mobile Broadband (eMBB) for the visual one. Furthermore, the metaverse user has to share the radio resources with classical eMBB users, characterized by an elastic nature. We formulate an optimization problem that determines the optimal resource sharing between flows, under various performance constraints. We show how to solve this problem in real-world scenarios where there is a discrete set of modulation and coding schemes (MCSs) and considering the various characteristics of the different flows.