# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 2 Related Work  
Many solutions have been proposed for enabling video QoS in SDN networks. [PERSON] and [PERSON] [8] proposed a framework for applying QoS in an SDN-based network. For QoS, they select the traffic route based on the shortest end-to-end delay metric, using the Dijkstra algorithm. The framework monitored the port utilization to reduce congestion and packet loss. Three types of applications, namely TCP, UDP, and VoIP, were evaluated based on reduced delay, jitter, and packet loss. [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2020) designed an OpenQoS multimedia controller to provide a dynamic end-to-end QoS routing, according to the network state. Their solution enhanced the video QoS for end-users by offering two paths: one is a QoS route specified for the multimedia, and the second is the shortest path for the other data. Using the LARAC algorithm, OpenQoS calculated the QoS paths that depend on the packet loss rate as routing metrics. To support dynamic rerouting of QoS packets, the controller monitored the network links, so in case of congestion, it invoked the routing algorithm to calculate a new path. However, the OpenQoS framework queried the network status each second. This would result in additional overhead for the controller to compute routing per network state, especially if the state had not changed significantly. To enhance network quality of service, [PERSON] et al. (2019) proposed SDN routing optimization based on reinforcement learning (RL). The proposed routing protocol used this artificial intelligence (AI) method to select the optimal paths with the least cost according to the network status. [PERSON] and [PERSON] (2019) presented a genetic algorithm-based routing method to provide efficient video delivery over SDN, called GA-SDN. It used chromosome fitness as a metric to choose the best path from multiple possible solutions. In case of congestion, the link weight will be increased to decrease fitness. Since their approach defined fitness as the reverse of path cost, when there was a higher fitness, it indicated a higher QoS. Similarly, [PERSON] et al. (2019) proposed a model for critical delay-sensitive telesurgery applications based on SDN networks. The model used a type-2 fuzzy system (T2 FS) and cuckoo optimization algorithm (COA) to generate reliable QoS routes. The model computed two paths, with the first set as the primary path and the other an alternative in case of failures; the delay metric was used as a link constraint to determine which routes are optimal between the remote surgeon and the operating robots at the patient's side. [PERSON] et al. (2019) developed a framework for QoS routing in SDN to enhance video streaming quality and best effort flows throughput. The authors leveraged SDN properties to create a consistent view of the network, consistent decisions, and a consistent enforcement strategy of rules. Their approach minimized the concentration of video streams on links to reduce packet loss and maximize QoS. [PERSON] et al. (2019) introduced an architecture that integrated autonomic and proactive QoS management into SDN environments. This architecture enabled QoS configuration on data plane devices. In addition, it monitored, predicted, and analyzed the network performance to achieve resources' optimizations and avoid degradations in the QoS. However, it focused on optimizing resource utilization without making any guarantees of meeting the service thresholds. [PERSON] et al. (2019) proposed a framework to enable QoS for business customer traffic and provide on-demand prioritization based on flow differentiation and resource reservations. Flows are classified based on the type of service (TOS) field and the destination IP to differentiate best effort traffic from business customer traffic. The authors applied the rate shaping technique to reserve queues, and they configured each router with a high priority queue and a low priority queue.