# Optimal Mobility-Aware Wireless Edge Cloud Support for the Metaverse
## 3 System Model
### Latency, Energy Consumption and Quality of Perception Experience
Clearly, the user experience could be elevated through viewing more AROs in foreground interactions or more delicate scenes from background models. Figure 7 reveals the variation in delay with the increasing foreground-interaction size. When the average foreground interaction size is not too large and there are still enough resources at target ECs, OptimNT and baseline schemes increase almost linearly at a similar speed. When the size keeps increasing and resources become limited, the CEC scheme becomes the most sensitive because it only targets the several closest ECs and it is easier to trigger the penalty. The OptimT scheme, on the other hand, maintains the least latency and the least increasing tendency. To this end, it could save up to 13.8% and 51.7% delay compared, respectively, to the OptimNT scheme and the CEC scheme. Figure 8 further reveals the variation in energy in this case. Baseline schemes process foreground interactions at ECs without considering energy. Hence, their decisions are not obviously affected by the size of foreground interactions and their energy consumption increases almost linearly. The OptimNT scheme keeps finding more suitable ECs according to current foreground-interaction size and remaining resources, while the OptimT schemes further enables terminals to process some tasks. Compared to the CEC scheme, they could save over 14.3% energy. It is necessary to point out that the energy consumption will not be taken into account when redirecting the request to the farther core cloud and triggering the penalty. According to Figure 9, the background model size is much larger and could cause a significant increase in delay. Note that the terminals could take charge of some foreground interactions and, hence, could make room for background models in the OptimT scheme. It is still the best scheme in terms of delay, which could be up to 9.8% less than the OptimNT scheme. As mentioned earlier, the proactive caching and processing of background models only happen at ECs; these schemes share a similar level of increased energy consumption until triggering the overloading penalty. Figure 7: Overall delay with foreground interaction size (6 EC, 30 requests, \(\mu=0.5\), EC capacity is 14 and total mobility probability is 1). The number of available VMs activated in an EC is known as the EC capacity. For a given EC capacity (e.g., 14), the ratio between the different number of requests (e.g., [30,40]) and the EC capacity could be applied to represent the average EC utilization in the network. Then, this rate was normalized into \([0,1]\) for better presentation. As shown in Figures 10 and 11, the increasing EC utilization rate indicates a more congested network and, hence, as expected, the delay and energy consumption increase as well. Compared to the OptimT scheme, the OptimNT scheme is still more sensitive in terms of energy but better in terms of delay. Thus, its consideration of terminals benefits delay at the cost of energy. Observe from Table 2, that even when there is no mobility event, the proposed OptimT  
Figure 8: Average energy consumption with foreground interaction size (6 EC, 30 requests, \(\mu=0.5\), EC capacity is 14 and total mobility probability is 1). Figure 9: Overall delay with background model size (6 EC, 30 requests, \(\mu=0.5\), EC capacity is 14 and total mobility probability is 1). scheme is still slightly better than other baseline schemes because its flexibility of terminals can better avoid potential EC overloading. Therefore, the proposed OptimT and OptimNT schemes have an obvious advantage over baseline schemes and are recommended in a congested network and a high-user-physical-mobility scenario. Especially when the MAR terminal still has enough energy capacity, its computing resources should not be neglected and, hence, the OptimT scheme is more suitable in this case. Figure 11: Energy consumption with average EC utilization rate (6 EC, \(\mu=0.5\) and total mobility probability is 1).