Heterogeneous networks offer a wide range of multimedia services, such as entertainment, advertising, and video conferences. In this multimedia scenario, users can access video content via heterogeneous wireless networks, such as LTE macro and small cells. Users also expect to receive real-time videos with Quality of Experience (QoE) support, which is a challenging task due to the great diversity of radio base stations in such heterogeneous environments. In this article, we introduce a Quality of Service (QoS-)/QoE-and Radio-aware (SER) handover management algorithm for heterogeneous networks to provide video dissemination with QoS/QoE support. SER algorithm considers the analytic hierarchy process (AHP) to adjust the degree of importance of each criterion in order to select the appropriate radio base station that the mobile node must connect, allowing efficient handover decision making for video transmission with high user experience. Simulation results show that the SER algorithm delivered videos with significant improvement on QoE than existing handover algorithms.

In Connected Autonomous Vehicles scenarios or CAV, ubiquitous connectivity will play a major role in the safety of the vehicles and passengers. The extensive amount of sensors in each vehicle will generate huge amounts of data that cannot be processed promptly by onboard units. Edge computing is a crucial solution to provide the required computation power and extremely low latency requirements for the future generation of CAVs. However, the high mobility of vehicles, together with dynamic 5G networking scenarios, poses a challenge to keep the services always close to the users, and therefore, keep the latency very low, such as expected by CAVs. In this paper, we propose MILT, a service migration algorithm for edge computing to perform predictive migration of services based on mobility prediction, available resources, and the quality level of the networks and applications. MILT supports a mobility-based handover prediction scheme to perform a pre-migration to the best available edge server while reducing the latency and increasing the processing capacity of the services of CAVs. Simulation results show the efficiency of the proposed algorithm in terms of latency, migration failures, and network throughput.

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