## 4 System Model
### Multi-Level with Multi-Edge Computing Architecture  
In this section, we will describe the multi-level, multi-edge, multi-user with multi-task system architecture in which the system architecture composed of three-level as shown in Figure 3. Staring from down to up, the first level consists of a set of \(\mathbb{N}\) VR game devices in which each device has a set of \(\mathbb{M}\) independent computation tasks that needed to be completed. In the above level, we have a set of \(\mathbb{K}\) mobile edge computing servers in which the VR game devices are distributed and connected via wireless channel. In addition, we have a backbone router which can connect and control the mobile edge computing server via wired connection as well as designed using SDN technology. Finally, in the last level, we have a single cloud server which can provide more resources as well as connect with a backbone router through the core network.  
```
1:Initialization: Each VR device \(i\) initializes the offloading decision for their computation tasks with \(\alpha_{i,j,0}=1,\forall i,j\)
2:for all each VR device \(i\) and at given time slot \(t\)do
3:for all each computation task \(j\)do
4:Uploads the computation tasks' requirements \(\{a_{i,j},b_{i},c_{i,j},\tau_{i,j},p_{i}^{T},p_{i}^{R},\zeta_{i}\}\) and the local computation capabilities \(f_{i}^{L}\) to the edge server.
5:Calculate the uplink and downlink data rate \(r_{i}^{TI},r_{i}^{D}\) for each VR device based on Equations (1) and (2).
6:Solve the optimization problem in Equation (16) and obtain the optimal computation offloading decision values \(\alpha_{i,j,k}\) for each compuatation task at VR devices in which the sum cost of the entire system is minimized.
7:Send the offloading decision values \(\alpha_{i,j,k}\) to each VR device.
8:endfor
9:endfor
```  
**Algorithm 1** Multi-Player Computation Offloading Algorithm  
Regarding the simulation and results for this architecture, there are some issues that should be handled for the computation offloading which are:  
* **Scalability Issue:** In the multi-edge environment, the number on VR game devices becomes large, which has more computation tasks that need to be offloaded and executed remotely. Simultaneously, the mobile edge computing servers should provide with the resources' scalability. Thus, an intelligent algorithm should be designed to scale the computation resources of the overloaded edge server using the computation resources of the neighbor edge server and cloud server which are underloaded.
* **Load Balancing Issue:** As mentioned above, the VR game devices are distributed across the edge computing servers. In addition, due to the randomization distribution of the users, some of the edge servers will be overloaded whereas other servers will be underloaded. Consequently,  
Figure 3: Multi-level, multi-edge, multi-user, with multi-task architecture.  
this will affect the computation offloading process and may lead to poor service quality and long delays due to network congestion. Therefore, it important to propose an efficient algorithm to balance the load between the edge server and improve the quality of service for the VR game device user.
* **Mobility Issue:** In the multi-edge environment, each VR game device has the ability to depart and leave dynamically between the edge servers within a computation offloading period which will be interesting and technically challenging, where the offloading decision and the execution location will be affected. Thus, an intelligent approach should be developed to determine the best execution place for the computation task in which the overall consumption in terms of time and energy will be minimized.
* **Possible Output:** Finally, if the scalability, load balancing and mobility issues are handled as mentioned above, the proposed model can operate and derive the computation offloading decision in an efficient manner. Further, the weighted sum cost of the entire system in terms of energy and time will be optimized.