# Performance Model for Video Service in 5G Networks
## 1 Introduction  
Fifth generation (5G) cellular networks are required to support cell capacity of multi-gigabit per second (Gbps) and cell edge throughput of tens of megabits per second (Mbps). In addition to the pure performance metrics, such as the rate, latency, reliability, and allowed connections, the scope of 5G incorporates the transformation of the mobile network ecosystem and accommodates heterogeneous services using one infrastructure [1]. To achieve this goal, 5G networks incorporate a technique named network slicing. Network slicing tries to slice the whole network into slices, each of which is tailored to the specific service requirements that are agreed to in a service level agreement (SLA) with customers (also known as tenants). Therefore, network slicing is an emerging business for operators that allows them to sell the customized network slices to various tenants at different prices [1]. As part of network slicing, radio access network (RAN) slicing defines a shared RAN connected to each of the multiple tenants' core networks, with radio resources distributed by the central controller to different tenants, to maximize the radio resource usage. To provide better-performing and cost-efficient services, RAN slicing is looking to intelligent radio resource management approaches to be aligned with users' activities per slice. For radio access networks, the spectrum is expensive; thus, guaranteeing high spectrum efficiency (SE) is meaningful. SLAs usually impose stringent requirements on the network quality of services (QoS). Therefore, it is critical to investigate how to intelligently respond to the dynamics of the traffic load from mobile users to obtain satisfactory QoS in each slice at an acceptable spectrum cost [1]. In particular, RAN providers must implement new radio resource management approaches to mapincoming SLA requirements associated with network slice requests into physical resources. Because of the implementation complexity, there is no explicit relationship between SLA requirements and physical resources to fulfill a service's QoS requirements. Furthermore, 5G networks are expected to provide diverse QoS guarantees for a wide range of services with various requirements. Therefore, there is increased difficulty in translating user-friendly business terms into resource-specific attributes in the 5G network. Considering these problems, we propose an approach to map customer-friendly business requirements to network attributes and achieve QoS assurance as part of the 5G RAN network design solution. Data analytics and machine learning (ML) using regression algorithm allow automated identification of relationships and dependencies between different parameters. For that reason, such approaches are exploited in the context of SLA mapping, given the need to correlate input parameters (high-level SLA requirements) with output parameters (i.e., low-level network attributes) [2]. For the QoS requirements specified in the SLA, a corresponding performance model can be provided. In this paper, the QoS performance model defines mapping between SLA high-level requirements and low-level network attributes. A performance model is constructed for each service, optimized using interference coordination approaches, and used to facilitate the network design and optimization process. We propose interference coordination (IC) approaches to optimize the constructed QoS performance model. There are static and dynamic IC approaches.