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With the emerging demand for high-definition videos in recent years, Multi-access Edge Computing (MEC) has become a promising solution to leverage Quality of Experience (QoE) of users in the 5G mobile network, which provides computing and cache resource at network edges to serve end users with less latency. Also, since mobile users tend to be influenced by the trends in social media, the performance of video caching will become more effective if we can extract the hidden information from interaction among them. In this paper, we propose a novel Collaborative Social-aware QoE-driven video Caching and Adaption (CSQCA) framework. Specifically, we first design a 2-tier MEC collaborative video caching architecture, which partially caches popular videos among multiple edge servers. Second, we propose a social-aware proactive cache strategy, which embeds interactions of users and video dissemination process in social networks into the caching mechanism. Third, a QoE-driven video adaptation algorithm is presented to dynamically transcode the cached videos into appropriate resolution on edge server for each request. Finally, we conduct our simulation based on real-world datasets. The simulation results show that the proposed CSQCA framework outperforms traditional cache algorithms, in terms of the average hit ratio and QoE.

In the future 5G networks, network deployment flexibility and low network latency are two of the most critical requirements and issues. Recently, network virtualization and Fog/Edge computing have been proposed as two potential solutions to enable the desired future network environment. This paper investigates the Virtual Network Embedding (VNE) problem in a Multi-access Edge Computing (MEC) architecture, according to the standards proposed by European Telecommunications Standards Institute (ETSI). We propose an embedding algorithm, called PSO-CSNR, to optimize end-to-end latency constraints in an MEC network. In addition, we adopt Activity on Vertex (AOV) network as our Virtual Network Request (VNR), which is more realistic to real applications. Moreover, we consider the latest processor technologies for substrate nodes, where the CPUs are deployed with asymmetric core frequencies, and propose the second algorithm, called DSS. The DSS can dynamically orchestrate the processing speed of each virtual function, in order to decrease the processing time of virtual functions on virtual nodes, so that the Infrastructure Providers (InPs) can gain more profit in the same amount of time. We then combine the PSO-CSNR with DSS, and refer to it as VNE-DSSO. The simulation results show that the VNE-DSSO algorithm outperforms the other existing algorithms in terms of revenue, acceptance ratio and embedding cost.