## 1 Introduction
Due to its safety and reliability, 5G technology can be used to ensure public safety, as well as to provide critical services, for example, in smart grids, the work of the police and security services, energy and water supply enterprises and healthcare. With the development of computation offloading for MEC system, several related system architectures and approaches have been proposed in which the intensive computation tasks of the mobile devices will be offloaded to a richer resource located at the edge of the radio access network (RAN) [18, 19]. However, most of these studies address only two levels of computation offloading in MEC systems [20, 21], while few studies address the multi-level computation offloading [22]. In addition, most of these studies address single user or multi-user with only single computation task in which minimizing energy consumption, allocating radio and computation resources efficiently, and/or satisfying latency requirements for mobile users are the main goals. Consequently, the main motivations behind our work are based on the following observations:* Considering a multi-level environment for multi-player computation offloading especially in the case of a large number of mobile devices and the resources of edge server are not enough is an important issue. * In the MEC system, most of the complex mobile applications have many tasks that need to be offloaded and executed. Therefore, addressing the multi-task issue is important. * Computing resources on the edge and cloud server and computing tasks of the mobile devices are considered to be the main factors that play an important role in the efficiency of multi-level, multi-player, multi-task edge-cloud computing system. It is, therefore, crucial to have an effective policy that is joint between them. Motivated by such considerations, in this paper, we introduced a reliable AR system able to support ultra-low latency AR applications with the announced specifications. This system has deployed a multi-level, multi-player with multi-task computation offloading environment to provide the computing resources at the edge of the RAN which can reduce the communication latency of the VR tasks. In addition, we formulated the computation offloading as an integer optimization problem whose objective is to minimize the sum cost entire system in terms of network latency and energy consumption. The main contributions of this paper include:  
* An efficient computation offloading model is formulated as an integer optimization problem with the objective of minimizing the sum cost of an entire system in terms of network latency and energy consumption for a multi-level multi-player multi-task edge-cloud computing systems. In addition, our environment has considered a single cloud computing which connected with the edge computing server via an intelligent core network that is built based on SDN technology to provide with more resources when the number of VR devices increases and the resources of edge server becomes not enough. * An efficient algorithm has been designed which provides comprehensive processes for deriving the optimal computation offloading decision. * Three main VR applications have been considered which are multiple player VR games, Holograms and 360-degree Rv video applications. * Finally, simulations have been conducted to validate our proposed model and prove that the network latency and energy consumption can be reduced by up to 26.2%, 27.2% and 10.9%, 12.2% in comparison with edge and cloud execution, respectively. In addition, we provide a prototype and real implementation for the proposed system using OpenAirInterface software. The rest of the paper is organized as follows. Section 2 introduces three main VR applications. Section 3 reviews related work on computation offloading policies. Section 4 presents our system model for multi-level multi-player with multi-task computation offloading and the designed algorithm. Simulation experiments and prototype implementation are conducted in Section 5. Finally, Section 6 concludes the paper.