# Optimal Mobility-Aware Wireless Edge Cloud Support for the Metaverse
## 2 Related Work  
Hereafter, a series of closely related works in the area edge/cloud support of metaverse-type applications over 5G and beyond wireless networks are discussed and compared with the approach proposed in this paper. Noticing that, despite their limitations, MAR terminals have witnessed a series of technical improvements, their joint utilization with ECs for network optimization can bring significant benefits. In [17], the authors aim to minimize the energy consumption of multicore smart devices, which are usually applied for AR applications. Through tracking the response process of an AR application, they manage to measure the terminal's energy consumption using [PERSON]'s law. Although the law and a similar framework for the terminal energy consumption are also applied in this paper, we consider a broader use-case scenario which includes edge servers and a more complex balance between energy and latency. The work in [18] shares a similar target to this paper, which relates to achieving a balance between latency and energy consumption under a level of acceptable image quality. However, ref. [18] brings in local sensors to MAR devices for recognizing and tracking AROs, so that their scheme can realize selective local visual tracking (optical flow) and selective image offloading. The object-recognition stage is more focused in [18], with four AR applications requiring different types of AROs. Without support from ECs, they utilize a further cloud server as a heavy database for 3D AROs and leave most tasks to the terminals, whilst cloud offloading is only triggered when confronted with a calibration. However, in this work, we consider an EC-supported network and compare the difference between schemes explicitly utilizing the MAR terminals, or not. In [19], the energy efficiency is optimized under required service latency for MAR in an EC-supported network. Similarly, the authors consider proactively caching and propose a tradeoff between energy and latency in terms of cache size. However, their mobile cache and power-management scheme still focuses the energy consumption on terminals. Clearly, the above-mentioned works do not explicitly consider user mobility, perception quality, service decomposition and metaverse application features, like ours. The problem of efficient resource allocation for supporting metaverse-type applications is starting to attract significant amount of attention and a plethora of aspects have already been considered. In [20], the emphasis is placed on the synchronization of Internet-of-Things services, in which they employ IoT devices to collect real-world data for virtual service providers. Through calculating maximum awards, users can select the ideal virtual service provider. Researchers then propose the game framework, which considers such a reward allocation scheme and general metaverse sensing model [20]. In [21], the authors also adopt a game theoretical framework by considering tasks offloading between mobile  
Figure 3: Illustrative toy examples of different activated nodes in metaverse. devices based on coded distributed computing in a proposed vehicular metaverse environment. Another framework, proposed by [22], manages and allocates different types of metaverse applications so that common resources among them can be shared through a semi-Markov decision process and an optimal admission-control scheme. The work in [23] applies a set of proposed resource-optimization schemes in a virtual education metaverse. More specifically, a stochastic optimal resource-allocation scheme is developed with the aim of reducing the overall cost incurred by a service provider. Similar to the service decomposition in this paper, they only upload and cache some parts of the data or services, to achieve reduced levels of delay and offer better privacy [23]. The work in [5] is closely related, since, in that paper, not only latency but also energy consumption is considered, as is the case for our proposed model, which uses a multi-objective optimization approach.