关键词： Streaming media   Resource management   Servers   Quality of experience   Clocks   Heuristic algorithms   Video recording   Streaming service   mobile edge computing   game theory   auction   social welfare  

摘要： The live video streaming services have been suffered from the limited backhaul capacity of the cellular core network and occasional congestions due to the cloud-based architecture. Mobile Edge Computing (MEC) brings the services from the centralized cloud to nearby network edge to improve the Quality of Experience (QoE) of cloud services, such as live video streaming services. Nevertheless, the resource at edge devices is still limited and should be allocated economically efficiently. In this paper, we propose Edge Combinatorial Clock Auction (ECCA) and Combinatorial Clock Auction in Stream (CCAS), two auction frameworks to improve the QoE of live video streaming services in the Edge-enabled cellular system. The edge system is the auctioneer who decides the backhaul capacity and caching space allocation and streamers are the bidders who request for the backhaul capacity and caching space to improve the video quality their audiences can watch. There are two key subproblems: the caching space value evaluations and allocations. We show that both problems can be solved by the proposed dynamic programming algorithms. The truth-telling property is guaranteed in both ECCA and CCAS. The simulation results show that the overall system utility can be significantly improved through the proposed system.

关键词： Adaptive streaming   DASH   Deep learning   Mobile edge computing   Adaptive algorithm  

摘要： Adaptive streaming has recently emerged as a technology enabling high-quality streaming at various bitrates. One of the video streaming challenges remains in research topic nowadays that is choosing optimal segment base on network characteristics and streaming devices, such as network bandwidth, latency, the computational capacities of devices. Researchers have proposed many algorithms to overcome such issues within their predefined conditions. However, those proposed methods do not perform efficiently in the heterogeneous network today. Consequently, in this article, we present research on a context-aware adaptive algorithm for ambient intelligence dynamic adaptive employing mobile edge computing (MEC). Specifically, we apply deep learning in the adaptive algorithm which is installed at the MEC to assist clients in choosing the optimal streaming segments as well as reduce network latency. Furthermore, we apply the multilayer perceptron classifier with data obtained from various experiments of adaptive streaming algorithms then combine them in a general algorithm. In the analysis, we use network simulator NS3 as a tool to carry out the verification of our proposed method. As a result, the proposed research reduces network latency as well as improve quality streaming compared to existing approaches.

关键词： mobile edge computing   auction theory   resource allocation   incentive mechanism  

摘要： Mobile edge computing (MEC) is a new computing paradigm, which provides computing support for resource-constrained user equipments (UEs). In this letter, we design an effective incentive framework to encourage MEC operators to provide computing service for UEs. The problem of jointly allocating communication and computing resources to maximize the revenue of MEC operators is studied. Based on auction theory, we design a multi-round iterative auction (MRIA) algorithm to solve the problem. Extensive simulations have been conducted to evaluate the performance of the proposed algorithm and it is shown that the proposed algorithm can significantly improve the overall revenue of MEC operators.

关键词： Data allocation   Differential evolution   loT   Mobile edge computing   Request processing  

摘要： Internet of Things (IoT) is an emergent communication technology connecting heterogeneous users, services and applications for granting seamless support for end-user demands. The heterogeneous nature of the devices and intelligent computing, storage, and processing paradigms are exploited for serving different application requests of the end-user devices. Edge computing inherits the functions of IoT platform to the brim of the user network to grant tardiness-free service/ communication support. This manuscript discusses a novel delay-aware concurrent data management (DCDM) method for IoT collaborative mobile edge computing (MEC) environment. This method categorizes request and response data independently in the edge layer through timeout and request density based optimization. A differential evolution (DE) based optimization is introduced for optimizing the constraints of the framework in favor of concurrent request processing and precise data allocation. This method is tested through appropriate experiments and the corresponding results are verified to prove the consistency of the method. The metrics such as serviced and processed request, response time, resource utilization, and communication span are validated using the experimental results. The performance of the proposed DCDM is compared with the existing methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   D2D communications   partial offloading   latency   resource allocation  

摘要： We consider Device-to-Device (D2D)-enabled mobile edge computing offloading scenario, where a device can partially offload its computation task to the edge server or exploit the computation resources of proximal devices. Keeping in view the millisecond-scale latency requirement in 5G service scenarios and the spectrum scarcity, we focus on minimizing the sum of task execution latency of all the devices in a shared spectrum with interference. In particular, we provide an integrated framework for partial offloading and interference management using orthogonal frequency-division multiple access (OFDMA) scheme. Accordingly, we formulate total latency minimization as a mixed integer nonlinear programming (MINLP) problem by considering desired energy consumption, partial offloading, and resource allocation constraints. We use decomposition approach to solve our problem and propose a novel scheme named Joint Partial Offloading and Resource Allocation (JPORA). With aim to reduce the task execution latency, JPORA iteratively adjusts data segmentation and solves the underlying problem of quality of service (QoS)-aware communication resource allocation to the cellular links, and interference-aware communication resource allocation to D2D links. Extensive evaluation results demonstrate that JPORA achieves the lowest latency as compared to the other baseline schemes, meanwhile limiting the local energy consumption of user devices.

关键词： Mobile edge computing   MEC   stochastic geometry   local delay   VANET  

摘要： Mobile edge computing (MEC) is a new computing paradigm for addressing users' ever-increasing demand for quality of service (QoS) in vehicular ad hoc networks (VANETs). The local delay is a performance metric that has a big impact on the QoS provisioned to users. This paper studies the analysis of the local delay in an MEC-based VANET based on stochastic geometry. An analytical model is first derived to analyze the average uplink local delay of a vehicle on a highway for sending a packet to an edge node along the highway. In deriving the model, the spatial distributions of vehicles and edge nodes are modeled as an independent one-dimensional (1-D) homogeneous Poisson point process (PPP). The edge node for accommodating the service request of a vehicle node is determined based on a nearest-receiver model. Moreover, a carrier sense multiple access (CSMA) mechanism is considered for channel access in the derived local delay model. The derived analytical model is validated through simulation results and the impacts of major parameters on the average uplink local delay are investigated. The derived model can be used to provide a basis for parameter settings in the deployment of edge nodes.

关键词： Age-of-information   mobile edge computing   computation-intensive  

摘要： In status update scenarios, the freshness of information is measured in terms of age-of-information (AoI), which essentially reflects the timeliness for real-time applications to transmit status update messages to a remote controller. For some applications, computational expensive and time consuming data processing is inevitable for status information of messages to be displayed. Mobile edge servers are equipped with adequate computation resources and they are placed close to users. Thus, mobile edge computing (MEC) can be a promising technology to reduce AoI for computation-intensive messages. In this paper, we study the AoI for computation-intensive messages with MEC, and consider three computing schemes: local computing, remote computing at the MEC server, and partial computing, i.e., some part of computing tasks are performed locally, and the rest is executed at the MEC server. Zero-wait policy is adopted in all three schemes. Specifically, in local computing, a new message is generated immediately after the previous one is revealed by computing. While in remote computing and partial computing, a new message is generated once the previous one is received by the remote MEC server. With infinite queue size and exponentially distributed transmission time, closed-form average AoI for exponentially distributed computing time is derived for the three computing schemes. For deterministic computing time, the average AoI is analyzed numerically. Simulation results show that by carefully partitioning the computing tasks, the average AoI in partial computing is the smallest compared to local computing and remote computing. The results also indicate numerically the conditions on which remote computing attains smaller average AoI compared with local computing.

关键词： Enterprise multimedia security   Resource allocation   Lyapunov optimization   Mobile edge computing  

摘要： Mobile edge computing (MEC) is a promising computing model and has gained remarkable popularity, as it deploys the resources (e.g., computation, network, and storage) to the evolved NodeB (eNB) to provide enormous benefits such as low delay and energy consumption. More and more enterprises construct their edge computing platforms to store multimedia contents (i.e., video, audio, photos, and text data) for the user equipment (UE). However, both the eNB and UEs will experience serious security attacks when transmitting or receiving multimedia data via the wireless network. Existing MEC studies mainly focus on task offloading and performance improvement without considering the enterprise multimedia security problem. This paper proposes a security and performance-aware resource allocation (Spara) algorithm for enterprise multimedia in MEC environment. More specifically, we first build the architecture of enterprise multimedia security for sending the data requests to UEs, which mainly consists of computing and bandwidth resource allocation. Then, we formulate the stochastic data transmission problem to minimize the delay and energy consumption of UEs subject to the security guarantee. To achieve this goal, two queues, namely front-end queue and back-end queue, are used for each UE, and the Lyapunov optimization technique is applied to determine how to allocate the computing and bandwidth resources. Rigorous theoretical analysis shows that Spara algorithm meets the [O(1/V), O(V)] energy-delay tradeoff. Extensive simulation experiments validate this analysis result and the effectiveness of Spara algorithm.

关键词： Adaptive bitrate streaming   video transcoding   mobile edge computing   wireless networks   deep reinforcement learning  

摘要： Adaptive bitrate (ABR) streaming has been used in wireless networks to deal with the time-varying wireless channels. Traditionally, wireless video is fetched from remote Internet server. However, wireless video streaming from Internet server faces challenges such as congestion and long latency. ABR streaming and video transcoding at the radio access network (RAN) edge have shown the potential to overcome such problem and provided better video streaming service. In the paper, we consider joint computation and communication for ABR streaming based on mobile edge computing (MEC) under time-varying wireless channels. We propose a joint video transcoding and quality adaptation framework for ABR streaming by enabling RAN with computing capability. By modeling the wireless channel as a finite state Markov channel, we formulate the optimization problem as a stochastic optimization problem of joint computational resource assignment and video quality adaptation for maximizing the average reward, which is defined as the tradeoff between user perceived quality of experience and the cost of performing transcoding at edge server. By using deep reinforcement learning (DRL) algorithm, we develop an automatic algorithm to perform the computational resource assignment and video quality adaptation without any prior knowledge of channel statistics. Simulation results using Tensorflow show the effectiveness of the designed MEC-enabled ABR streaming system and DRL algorithm.

关键词： Mobile edge computing   latency minimization   physical layer security   joint optimization  

摘要： This letter proposes a low-latency secure mobile edge computing (MEC) system where multiple users offload computing tasks to a base station in the presence of an eavesdropper. We jointly optimize the users' transmit power, computing capacity allocation, and user association to minimize the computing and transmission latencies over all users subject to security and computing resource constraints. Numerical results show that our proposed algorithm outperforms baseline strategies. Furthermore, we highlight a novel trade-off between the latency and security of MEC systems.