Multitier EdgetoCloud Architecture for Adaptive Video Delivery In the last few years, there has been a rapid proliferation of a wide range of realtime video services and applications. These technologies flood the wireless systems with video content on a daily basis. As a result of this sharp increase in video traffic, the prospect of errors due to network interference and congestion rises. Incidentally, the adoption of the 5th generation of wireless systems (5G) will allow this growth to be even greater due to its high bandwidth capacity and low latency. However, even with these improvements on the wireless capabilities, a reliable and highquality video transmission still imposes several challenges, such as how to handle a large number of heterogeneous devices and how to better use the resourcericher Edge, Fog, and Cloud computing sources to meet the useru0027s requirements. To overcome these issues, this work proposes a multitier video delivery architecture relying upon several technologies such as Multiaccess Edge computing (MEC), 5G slices, and microservice placementchaining. Furthermore, to assess the proposed idea an experimental proofofconcept testbed of the multitier architecture was designed, implemented, and evaluated using realworld tools and actual video sequences. The results obtained supported our claim that a multitier video delivery system is feasible and can greatly benefit the endusers.

Cost and Energy Aware Dynamic Splitting of Video Traffic in Heterogeneous Networks The vision towards 5G and beyond is to provide remarkable performance enhancements that enable the launch of new services and markets in different industry verticals. Example scenarios of those services include indoor hotspot, broadband access in a crowd, and dense urban; many of which require very high data rates. Traffic offloading and devicetodevice cooperation have been leveraged to expand the system capacity and coverage through using heterogeneous network technologies. In this work, we shed the light on the significant gains incurred when predicted short term network performance is factored into network splitting decisions over multiple wireless interfaces, while guaranteeing a desired quality of experience to end users. Accordingly, we allow dynamic use of multiple network interfaces taking into consideration their energy requirement and price models to deliver premium services and minimize the overall energy consumption and total cost. We develop a novel and efficient realtime traffic splitting approach that makes use of predicted bit rate of each network interface in addition to devicetodevice cooperation to decide on the amount of video traffic to be delivered on each interface at every time slot. The proposed approach is validated and evaluated under realistic network conditions. Simulation results demonstrate substantial gains in terms of energy consumption, data cost and quality of user experience as compared to multiple alternative solutions.