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 multitenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.

5G mobile systems are expected to meet different strict requirements beyond the traditional operator use cases. Effectively, to accommodate needs of new industry segments such as health care or manufacturing, 5G systems need to accommodate elasticity, flexibility, dynamicity, scalability, manageability, agility and customization along with different levels of service delivery parameters according with the service requirements. This is currently possible only by running the networks on top of the same infrastructure, technology named network function virtualization, through this sharing the development and infrastructure costs between the different networks.In this paper, we showcase the need for the deep customization of mobile networks at different granularity levels: per network, per application, per group of users, per individual users and even per data of users. The paper also assesses the potential of network slicing to provide the appropriate customization and highlights the technology challenges. Finally, a high level architectural solution is proposed addressing a massive multi-slice environment.

5G system and beyond will build on the network slicing for offering high customizable services with different requirements that run on top of the same shared infrastructure. Each network slice, such as Ultra-Reliable Low latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB), has different requirements that can be even contradicting from a slice to another. A network slice consists of a set of physical or virtual network functions (VNF/PNF) that have various capabilities and run across multiple administrative and cloud domains of different technology. A user can simultaneously request multiple services from different network slices. In this paper, we address the problem of initial placement and live migration of multiple mobile services across centralized and edge cloud by taking into account service types, network conditions and users' mobility features. As a solution to this problem, in this paper, we suggest and evaluate a solution that orchestrates the network services in a cost-efficient way, ensuring that each user could be simultaneously served by multiple slices while perceiving a high QoS and ensuring that the service level agreements (SLAs) of the consumed services are not violated.

Social Multimedia Networks (SMNs) have attracted much attention from both academia and industry due to their impact on our daily lives. The requirements of SMN users are increasing along with time, which make the satisfaction of those requirements a very challenging process. One important challenge facing SMNs consists of their internal users that can upload and manipulate insecure, untrusted and unauthorized contents. For this purpose, controlling and verifying content delivered to end-users is becoming a highly challenging process. So far, many researchers have investigated the possibilities of implementing a trustworthy SMN. In this vein, the aim of this paper is to propose a framework that allows collaboration between humans and machines to ensure secure delivery of trusted videos content over SMNs while ensuring an optimal deployment cost in the form of CPU, RAM, and storage. The key concepts beneath the proposed framework consist in i) assigning to each user a level of trust based on his/her history, ii) creating an intelligent agent that decides which content can be automatically published on the network and which content should be reviewed or rejected, and iii) checking the videos' integrity and delivery during the streaming process. Accordingly, we ensure that the trust level of the SMNs increases. Simultaneously, efficient Capital Expenditure (CAPEX) and Operational Expenditures (OPEX) can be achieved. Index Terms-Social multimedia network, video streaming, trust model, and trust management. • Secure storage: this component mainly works as follows: first of all, an authorized user sends an upload request

Given the constantly growing demand for live streaming services, live transcoding has become compulsory and very challenging. So far, investigations have been confined to satisfy a huge number of users for ensuring the Quality of Experience (QoE). The aim of this paper is to propose a framework architecture following ESTI-NFV (Network Function Virtualization) model [1], whereby the transcoding and streaming Virtual Network Functions (VNFs) would be running on top of multiple cloud domains. By respecting ESTI-NFV model, we ensure the flexibility of our virtual delivery platform that scales up/down and in/out relative to the changing demands of the endusers in order to reduce cost. For this purpose, this paper presents a new framework for managing the virtual live transcoding and streaming VNFs on top of multiple cloud domains for ensuring the QoE while reducing the cost. In order to develop such a framework, we have done a set of experimental benchmarking of transcoding and streaming VNFs using variant flavors (i.e., in terms of CPU and Memory resources). The obtained results will be explored later for developing an intelligent algorithm that will be integrated with the proposed framework in managing different transcoding and streaming VNFs in an efficient manner.