The emergence of intelligent applications produces the demand for computing. How to reduce the computation pressure in mobile-edge computing (MEC) under massive computation demand is an urgent problem to solve. Specifically, the allocation of heterogeneous resources, including communication resources and computing resources, needs to be optimized simultaneously. From the perspective of joint optimization of channel allocation, device-to-device (D2D) pairing, and offloading mode, this article studies the multiuser computing task offloading problem in device-enhanced MEC. The objective is maximizing the aggregate offloading benefits, i.e., the tradeoff between delay and energy consumption, of all compute-intensive users in the network. By introducing game theory, the problem is modeled as a multiuser computation task offloading game, which is proved to be an exact potential game (EPG) with at least one pure-strategy Nash equilibrium (NE) solution. In order to find a desirable solution, this article proposes a better reply-based distributed multiuser computation task offloading algorithm (BR-DMCTO). Simulation results show that the proposed offloading mechanism can improve the benefit of users, and verify the effectiveness and convergence of the proposed algorithm.

Recently, the development of unmanned aerial vehicle (UAV) mobile-edge computing (MEC) networks has brought unprecedented gains and opportunities. In this article, the joint computation offloading, UAV role, and location selection problem in hierarchical multicoalition UAV MEC network is investigated. To capture the hierarchical feature and discrete optimization, the discrete Stackelberg game with multiple leaders and followers is formulated. We prove that both the leader-level and member-level subgames are ordinal potential games (OPGs) with Nash equilibrium (NE). Thus, the Stackelberg equilibrium (SE) is guaranteed. To achieve the SE, the log-linear-based hierarchical learning algorithm (LHLA) is proposed and analyzed. The simulation results show that the LHLA can converge fast and achieve better performance compared with the existing schemes.

In this letter, we investigate the joint optimization of computation offloading, channel allocation and position deployment problem for coalition-based unmanned aerial vehicle (UAV) mobile edge computing networks. To characterize the hierarchical offloading feature in coalition-based UAV network, we propose the hierarchical framework and formulate a discrete multi-leader multi-follower energy minimization Stackelberg game. In addition, we prove that the multi-leader subgame and multi-follower subgame are exact potential games with Nash equilibrium. Then the existence of Stackelberg equilibrium (SE) is guaranteed. The spatial adaptive play-best response based hierarchical iterative learning (SAP-BRHIL) algorithm is proposed to achieve the SE. The simulation results show that the proposed SAP-BRHIL algorithm can achieve the lowest network energy consumption compared with some existing approaches.

The motion-compensated temporal filtering (MCTF)-based scalable video coding (SVC) provides a full scalability including spatial, temporal and signal-to-noise ratio (SNR) scalability with fine granularity, each of which may result in different visual effect. This paper addresses a novel approach of two-dimensional unequal error protection (2D UEP) for the scalable video with a combined temporal and quality (SNR) scalability over packet-erasure channel. The bit-stream is divided into scalable subbitstreams based on the structure of MCTF. Each subbitstream is further divided into several quality layers. Unequal quantities of bits are allocated to protect different layers to obtain acceptable quality video with smooth degradation under different transmission error conditions. Experimental results are presented to show the advantage of the proposed 2D UEP scheme over the traditional one-dimensional unequal error protection (1D UEP) scheme. Comparing the proposed method with the 1D UEP scheme on SNR layers, our method gives up to 0.81-dB improvement for some video sequences

In the mobile edge computing (MEC) networks, the heterogeneous demands can be met through device-to-device (D2D) technology due to the idle local resources from the neighbors. Although the overload of mobile edge computing servers can be relieved by sharing idle local resources among neighbors, the active impact of the cooperation in the close neighbors is ignored in the most existing works. In this paper, we study the mutual cooperation between the users with heterogeneous demands such as the computation tasks or the desired contents, and then propose an innovative D2D-assisted heterogeneous users cooperation framework where two users with different demands can share their idle local resources. The optimization is to minimize the aggregate delay of all users in MEC networks. First, the problem is formulated as a heterogeneous demands based matching game involving D2D matching and edge matching. Next, the multi-round cooperation matching (MRCM) algorithm is proposed to find the stable match of the proposed game. Finally, the simulation results present the validity and effectiveness of our proposed game as well as the proposed algorithm.

This paper is the first to investigate both communication and control in traffic channel (TCH) and control channel (CCH) respectively when considering leader-follower formation keeping in UAV communication networks. In this paper, we analyze the relationship between the mutual interference and information exchange cost, and then formulate the joint channel and link selection problem as a two-way consensus game between CCH and TCH. To characterize the two-way choice of link selection, we creatively propose the generalized two-way consensus equilibrium (GTCE) to capture the stable state. Then, we prove that the formulated game has at least one pure-strategy GTCE which can maximize the UAV communication network utility. A distributed better reply based joint channel and link selection (BRJCLS) algorithm as well as two-dimensional minimum spanning tree (MST) based initialization (TMSTI) algorithm are proposed to achieve the GTCE. Simulation results are presented to show the convergence and effectiveness of the formulated two-way consensus game and proposed algorithms.