Multi-access edge computing (MEC) enables placing video content at the edge of the network aiming to improve the quality of experience (QoE) of the mobile clients. Video content caching at edge servers also reduces traffic in the backhaul of the mobile network, hence reducing operational costs for mobile network operators (MNOs). However, minimizing the rate of cache misses and maximizing the average video quality may sometimes be at odds with each other, particularly when the cache size is constrained. Our objective in this article is two fold: First, we explore the impact of fixed video content caching on the optimal QoE of mobile clients in a setup where servers at mobile network edge handle bitrate selection. Second, we want to investigate the effect of cache replacement on QoE-traffic trade-off. An integer nonlinear programming (INLP) optimization model is formulated for the problem of jointly maximizing the QoE, the fairness as well as minimizing overall data traffic on the origin video server. Due to its NP-Hardness, we then present a low complexity greedy-based algorithm with minimum need for parameter tuning which can be easily deployed. We show through simulations that the joint optimization indeed enables striking a desired trade-off between traffic reduction and QoE. The results also reveal that with fixed cached contents, the impact of caching on the QoE is proportional to the desired operational point of MNO. Furthermore, the effect of cache replacement on QoE is less noticeable compared to its effect on backhaul traffic when cache size is constrained.
Multi-access edge computing (MEC) concept proposed by European Telecommunication Standard Institute (ETSI) is a key solution toward reducing the contention in the backhaul network @cite_12 @cite_16 . @cite_23 provide a comprehensive survey on the advantages of MEC architecture compared to centralized cloud-based framework from the perspective of different network functionalities. Placing content at the network edge has been shown to reduce the end-to-end latency and degrade the increased traffic on the origin server @cite_27 @cite_14 . In computationally intensive video processing applications such as augmented reality (AR) or virtual reality (VR), computing tasks of mobile clients can be executed at the edge servers, which reduces the computational burden of a central cloud. The potential of edge caching in reducing duplicate access of clients to popular content located on the origin server has also been demonstrated. @cite_27 analyze the impact of content caching at different layers of the mobile network.
@cite_14 @cite_27 @cite_23 @cite_16 @cite_12
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In this paper, we present a Mobile Edge Computing (MEC) scheme for enabling network edge-assisted video adaptation based on MPEG-DASH (Dynamic Adaptive Streaming over HTTP). In contrast to the traditional over-the-top (OTT) adaptation performed by DASH clients, the MEC server at the mobile network edge can capture radio access network (RAN) conditions through its intrinsic Radio Network Information Service (RNIS) function, and use the knowledge to provide guidance to clients so that they can perform more intelligent video adaptation. In order to support such MEC-assisted DASH video adaptation, the MEC server needs to locally cache the most popular content segments at the qualities that can be supported by the current network throughput. Towards this end, we introduce a two-dimensional user Quality-of-Experience (QoE)-driven algorithm for making caching replacement decisions based on both content context (e.g., segment popularity) and network context (e.g., RAN downlink throughput). We conducted experiments by deploying a prototype MEC server at a real LTE-A based network testbed. The results show that our QoE-driven algorithm is able to achieve significant improvement on user QoE over 2 benchmark schemes. Mobile edge computing (MEC) literally pushes cloud computing from remote datacenters to the life radius of end users. By leveraging the widely adopted ETSI network function virtualization (NFV) architecture, MEC provisions elastic and resilient mobile edge applications with proximity. Typical MEC virtualization infrastructure allows configurable placement policy to deploy mobile edge applications as virtual machines (VMs): affinity can be used to put VMs on the same host for inter-VM networking performance, while anti-affinity is to separate VMs for high availability. In this paper, we propose a novel model to track the availability and cost impact from placement policy changes of the mobile edge applications. We formulate our model as a stochastic programming problem. To minimize complexity challenge, we also propose a heuristic algorithm. With our model, the unit resource cost increases when there are less resources left on a host. Applying affinity would take up more resources of the host but saves network bandwidth cost because of co-location. When enforcing anti-affinity, experimental results show increases of both availability and inter-host network bandwidth cost. For applications with different resource requirements, our model is able to find their sweet points with the consideration of both resource cost and application availability, which is vital in a less robust MEC cloud environment. Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges. Mobile edge computing (MEC) is an emerging technology that aims at pushing applications and content close to the users (e.g., at base stations, access points, and aggregation networks) to reduce latency, improve quality of experience, and ensure highly efficient network operation and service delivery. It principally relies on virtualization-enabled MEC servers with limited capacity at the edge of the network. One key issue is to dimension such systems in terms of server size, server number, and server operation area to meet MEC goals. In this paper, we formulate this problem as a mixed integer linear program. We then propose a graph-based algorithm that, taking into account a maximum MEC server capacity, provides a partition of MEC clusters, which consolidates as many communications as possible at the edge. We use a dataset of mobile communications to extensively evaluate them with real world spatio-temporal human dynamics. In addition to quantifying macroscopic MEC benefits, the evaluation shows that our algorithm provides MEC area partitions that largely offload the core, thus pushing the load at the edge (e.g., with 10 small MEC servers between 55 and 64 of the traffic stay at the edge), and that are well balanced through time.