In this paper, we investigate the allocation of resource in D2D-aided Fog computing system with multiple mobile user equipments (MUEs). We consider each MUE has a request for task from a task library and needs to make a decision on task performing with a selection of three processing modes which include local mode, fog offloading mode, and cloud offloading mode. Two scenarios are considered in this paper, which mean task caching and its optimization in off-peak time, task offloading, and its optimization in immediate time. In particular, task caching refers to cache the completed task application and its related data. In the first scenario, to maximize the average utility of MUEs, a task caching optimization problem is formulated with stochastic theory and is solved by a GA-based task caching algorithm. In the second scenario, to maximize the total utility of system, the task offloading and resource optimization problem is formulated as a mixed integer nonlinear programming problem (MINLP) with a joint consideration of the MUE allocation policy, task offloading policy, and computational resource allocation policy. Due to the nonconvex of the problem, we transform it into multi-MUEs association problem (MMAP) and mixed Fog/Cloud task offloading optimization problem (MFCOOP). The former problem is solved by a Gini coefficient-based MUEs allocation algorithm which can select the most proper MUEs who contribute more to the total utility. The task offloading optimization problem is proved as a potential game and solved by a distributed algorithm with Lagrange multiplier. At last, the simulations show the effectiveness of the proposed scheme with the comparison of other baseline schemes.

To mitigate the impact of the dramatic growth of wireless video traffic and provide users with heterogeneous video services, a collaborative caching-transmission method in cache-enabled terahertz (THz) heterogeneous networks is studied in this paper. We propose a novel scalable video coding (SVC)-based two-tier cooperative caching strategy that can provide video services with different perceptual qualities. In addition, we propose a hierarchical hybrid transmission scheme where users obtain the requested layer files through the cooperative cellular multicast-D2D and THz transmissions. A base layer-based multicast group aggregation process is presented to improve resource utilization, while an efficient multicast transmission resource scheduling scheme can make better use of the spatial reusability. We derive the expressions for the average offloading capacity gain and average energy consumption, and formulate an efficiency function optimization problem. The problem is efficiently solved by the greedy strategy-gradient descent based caching solution and long tail distribution based heuristic caching solution. Simulation results validate the two solutions and demonstrate that the proposed caching-transmission method can achieve quasi-optimal performance compared with other contrasting schemes.

In cellular vehicular communications, high density and mobility of vehicles require frequent resource allocation, which can cause network congestion and large signalling and processing delay. To overcome this problem, we propose a novel semi-persistent resource allocation scheme based on two-tier heterogeneous network architecture. The architecture includes a central macro base station (MBS) and multiple roadside units (RSU). In the proposed semi-persistent scheme, the MBS pre-allocates persistent resource to RSUs based on predicted traffic and then allocates dynamic resource upon real-time requests from RSUs while vehicles simultaneously communicate using the pre-allocated resource. A simple Space-Time k-Nearest Neighbour (ST-kNN) method is developed for short-term traffic prediction, and a geometric water-filling algorithm is developed for minimizing the relative latency. Simulation results validate the effectiveness of the proposed semi-persistent scheme in comparison with two benchmark schemes.

As the demand of data-hungry and computing intensive tasks grows dramatically, cache-aided device-to-device multicast (D2MD) netwoks are introduced, which can offload traffic from the base stations (BS) to D2D users (DUEs) directly to alleviate the heavy burden on backhaul links and improve the energy and spectrum efficiency. However, most previous works ignored the limitation of battery power and scarce computing capabilities of DUEs. In this paper, we study the computation and traffic offloading in cache aided D2MD networks for the content delivery and delay sensitive task offloading services. Firstly, in order to provide stable multicast links and enhanced computing resources, a D2D cluster head (DCH) selection strategy is proposed which jointly considers the social attributes, available energy and transfer rate of DUEs. Secondly, to improve the efficiency of content distribution and optimize the energy consumption of content delivery, we propose a novel multicastaware coded and cooperative caching scheme which may increase the opportunity for D2D multicasting to obtain the desired contents. Thirdly, considering the DUEs association, uplink full duplex (FD) DCH transmission power allocation, and mobile edge computing (MEC) computation resource scheduling, an optimization computation offloading model is formulated. On this basis, we model the computation offloading and resource allocation optimization problem. Furthermore, we transform this problem into user allocation optimization problem (UAOP) and resource allocation optimization problem (RAOP), and RAOP is proved as a convex problem, and the optimal resource allocation solution is found. Finally, the simulation results show that our proposed schemes can effectively decrease the energy consumption and computing costs.