Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. Our goal is to acquire an online algorithm that optimally adapts task offloading decisions and wireless resource allocations to the time-varying wireless channel conditions. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To tackle this problem, we propose a Deep Reinforcement learning-based Online Offloading (DROO) framework that implements a deep neural network as a scalable solution that learns the binary offloading decisions from the experience. It eliminates the need of solving combinatorial optimization problems, and thus greatly reduces the computational complexity especially in large-size networks. To further reduce the complexity, we propose an adaptive procedure that automatically adjusts the parameters of the DROO algorithm on the fly. Numerical results show that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computation time by more than an order of magnitude compared with existing optimization methods. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment.Index Terms-Mobile-edge computing, wireless power transfer, reinforcement learning, resource allocation. ! • L. Huang is with the College

Abstract-An important issue of supporting multi-user video streaming over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resources while, at the same time, to meet each video's Quality of Service (QoS) requirement. In this work, we study the problem of video streaming over multi-channel multi-radio multihop wireless networks, and develop fully distributed scheduling schemes with the goals of minimizing the video distortion and achieving certain fairness. We first construct a general distortion model according to the network's transmission mechanism, as well as the rate distortion characteristics of the video. Then, we formulate the scheduling as a convex optimization problem, and propose a distributed solution by jointly considering channel assignment, rate allocation, and routing. Specifically, each stream strikes a balance between the selfish motivation of minimizing video distortion and the global performance of minimizing network congestions. Furthermore, we extend the proposed scheduling scheme by addressing the fairness problem. Unlike prior works that target at users' bandwidth or demand fairness, we propose a media-aware distortion-fairness strategy which is aware of the characteristics of video frames and ensures maxmin distortion-fairness sharing among multiple video streams. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes.

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In this work, we investigate the properties of energyefficiency (EE) and spectrum-efficiency (SE) for video streaming over mobile ad hoc networks by developing an energy-spectrumaware scheduling (ESAS) scheme. To describe a practical mobile scenario, we use a random walk mobility model, in which each node can choose its mobility direction and velocity randomly and independently. Through rigorous analysis and extensive simulations, we demonstrate that the node mobility is beneficial to EE but not to SE. The contributions of this work are twofold: 1) We propose an ESAS scheme with a dynamic transmission range, which significantly outperforms the previous minimum-distortion video scheduling in terms of joint EE and SE performance; 2) We derive an achievable EE-SE tradeoff range and a tight upper/lower bound with respect to energy-spectrum efficiency index for various node velocities. We believe that this work helps to shed insights on the fundamental design guidelines on building an energy and spectrum efficient mobile video transmission system.Index Terms-Energy-efficiency, spectrum-efficiency, mobile ad hoc networks, multimedia communications.

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