The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that has been of great interest for decades for both, science and industry. The CVRP is a variant of the vehicle routing problem characterized by capacity constrained vehicles. The aim is to plan tours for vehicles to supply a given number of customers as efficiently as possible. The problem is the combinatorial explosion of possible solutions, which increases superexponentially with the number of customers. Classical solutions provide good approximations to the globally optimal solution. D-Wave's quantum annealer is a machine designed to solve optimization problems. This machine uses quantum effects to speed up computation time compared to classic computers. The problem on solving the CVRP on the quantum annealer is the particular formulation of the optimization problem. For this, it has to be mapped onto a quadratic unconstrained binary optimization (QUBO) problem. Complex optimization problems such as the CVRP can be translated to smaller subproblems and thus enable a sequential solution of the partitioned problem. This work presents a quantum-classic hybrid solution method for the CVRP. It clarifies whether the implemenation of such a method pays off in comparison to existing classical solution methods regarding computation time and solution quality. Several approaches to solving the CVRP are elaborated, the arising problems are discussed, and the results are evaluated in terms of solution quality and computation time.

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Encoding multimedia streams of video calls is a very compute-intense task that significantly decreases battery lifetime of mobile phones. This paper introduces an approach to reduce power consumption of mobile phones by offloading video encoding efforts from mobile devices to external services. These services are hosted on servers co-located with cellular base stations. The paper describes how these services are integrated into the existing mobile network architecture and presents a communication protocol for negotiating offloading settings. First measurement results indicate that power consumption of mobile devices is reduced by approximately 13%.

46 1. Introduction During the last decade, growing and expanding mobile devices, such as smartphones, have accelerated evolution of Information and Communication Technology, especially cloud computing and wireless communications. In cloud computing, currently, many cloud services, such as file sharing, Content Delivery Network (CDN) service, and machine learning, are provided by several cloud vendors, such as Google 1) , Amazon 2) and Microsoft 3). In wireless communications, the mobile devices equip various network interfaces, such as Long Term Evolution (LTE), Wi-Fi and Bluetooth, and can connect to "the cloud" anytime and anywhere. As a result, the mobile devices, especially smartphones, are selected as a main platform of mobile services, and nowadays, the smartphones' owners can easily experience rich multimedia applications, such as 4K video streaming, Augmented Reality (AR) and Virtual Reality (VR), via the wireless networks. Moreover, not only the smartphones, but also sensor devices and surveillance video cameras are connected via the wireless networks. In Internet of Things (IoT) services, the sensor devices, including network cameras, are installed everywhere, and enable to monitor city streets, social infrastructures and nature environments in real time. Because the sensor devices have less computational resources and battery, the sensing data and captured videos are uploaded and analyzed in the "centralized" cloud 40). This behavior is a typical example of "computational offloading." The computational offloading indicates that computational tasks are transferred to external computing environments, such as cloud servers, via wired/wireless networks and executed in the external computing environments instead of an own device. According to the Cisco's report 4) , Cisco forecasts that the mobile traffic will increase seven-fold from 2016 to 2021, and mobile video accounts for approximately 80% of all mobile data in 2021. Thus, due to the centralized cloud, the plenty sensor data and rich contents may trigger the overloaded computing and severe network congestion, especially in the cloud-side backbone network, and invoke longer latency for data exchange between the cloud and end devices. To reduce the computational load in the cloud and to reduce backbone network traffic, cloudlet based computational offloading is proposed 5). Cloudlet (like a mini-cloud or a private cloud) is deployed in the physical proximity to users, such as in a shop and a restaurant, and accessed by using Wi-Fi (i.e., Cloudlet covers a small area). Although the cloudlet may provide the low-latency network connection, this tiny cloud has few computation and covers only few users. Recently, to cover a larger region, and to provide low-latency connectivity and resourceful computing, Mobile Abstract Recently, to provide a low-latency mobile computing platform, Mobile Edge Computing (MEC) is proposed. In this paper, we first summarized the feature capabilities of MEC, such as content distribution and caching, computational offloading and...