Mobile services are becoming more diverse, making them have different demands on communications, computing, and caching (3C) resources in mobile systems. Unlike the traditional work that considers only one type of service, this paper designs a unified framework to characterize the different kinds of services, and jointly optimizes the 3C resources of the base station (BS) and mobile devices to provide differentiated quality of service (QoS) for diverse services. In the proposed framework, we model the task required by the mobile device to be generated at the BS, the mobile device, or both of them, which means the requested tasks are served through different paths, consuming different bandwidth, computing and caching resources. Since diverse services have different QoS, we formulate a multi-objective programming (MOP) to optimize the allocation of the 3C resources for minimizing the total delay while maximizing the number of executed tasks requested by the mobile devices. We transform the MOP problem as a multi-objective Markov decision process (MO-MDP) and design a multi-objective proximal policy optimization (MO-PPO) algorithm to solve the MO-MDP. The proposed MO-PPO first trains two sub-policies separately for the two objectives, and then combines them to search for Pareto dominating solutions. By alternately perform the separate training and the combination, we can finally obtain a set of Pareto optimal solutions and the corresponding Pareto front. Simulation results show that the proposed MO-PPO outperforms traditional methods in finding a higher-quality set of Pareto optimal solutions and can more appropriately allocate 3C resources to different types of services.

Edge computing has been regarded as an efficient approach to achieve the multiplayer interactive virtual reality (VR) game over wireless networks, where the game scenes can be rendered at the edge computing server and then the real-time video frames can be transmitted to the players. In order to ensure the fairness of the interacting players, we allocate the edge computing and wireless bandwidth resources for minimizing the average inter-player delay among different players. The proposed programming is under the constraints of the absolute delay requirements, the maximum frame per second (FPS) demands, the total bandwidth and rendering resources limits. The influence of prediction and pre-rendering field of views (FOVs) is also considered in the optimization problem to model the interaction among players. To tackle the non-convex problem efficiently, a sub-optimal algorithm which convert the original problem into several convex subproblems to optimize iteratively is designed. Finally, numerical results verify the proposed algorithm can reduce the average interplayer delay significantly with lower complexity, and it also reveals the impact of the content sizes and the channel state conditions on the edge resource allocation.

Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a general coded caching with device computing strategy for computational tasks, e.g., virtual reality (VR) rendering, to minimize the average transmission bandwidth under the quality of service guarantee. Because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether a mobile device stores the data before computing or the data after computing, which tasks to be coded cached and which tasks to be computed locally. The optimization problem is shown to be 0–1 non-convex non-smooth programming, and can be decomposed into a computation offloading programming and a coded caching programming. For a computation offloading subproblem, we proposed an algorithm which applies the convergence of the alternating direction method of multipliers (ADMM) under a non-convex programming and the wide application of the concave-convex procedure (CCCP) for difference of convex (DC) programming to obtain a stationary point, and numerical results verify the convergence of the proposed algorithm and its suboptimality. For the coded cache programming, we design a low complexity algorithm to obtain an acceptable solution. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices.

Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a coded caching with device computing scheme for content computation, e.g., VR rendering, to minimize the average transmission bandwidth. Because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether the mobile devices cache the input data or the output data, which tasks to be coded cached and which tasks to compute locally. The optimization problem is shown to be 0-1 nonconvex nonsmooth programming which can be decomposed into the computation programming and the coded caching programming. Computation programming can be solved by utilizing the alternating direction method of multipliers (ADMM), and then a low complexity algorithm is proposed to obtain the acceptable solution for the coded cache programming. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices.