# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 2 Related Work
In addition, the FlowQoS ([PERSON] et al., 2019) system utilized SDN to provide per-flow QoS for broadband access networks. FlowQoS depended on classification and rate shaping based on the policies defined by the user. This system created a virtual switch topology inside the router and configured each switch according to a user-defined rate. Similarly, [PERSON] and [PERSON] (2019) implemented differentiated services on SDN networks to enhance the quality of service. They distinguished the flow within the network by changing the differentiated service code point (DSCP) value in the ToS field and then assigning different queues to each flow based on the DSCP value. When necessary, their proposed method shifted the flows between switch queues to prevent increasing delays, while utilizing available capacity within other switch queues to accommodate new flows. [PERSON] et al. [20] described an architecture for monitoring urban traffic in emergencies based on SDN. Their approach combined SDNs and Internet of Things (IoT) networks for more effective management of emergency resources. Their architecture enabled the modification of vehicle routes dynamically by changing traffic lights to facilitate the movement of emergency service units. [PERSON] et al. [21] proposed a multimedia traffic management system based on the QoE estimation scheme and traffic pattern classification for SDN networks. They implemented two models. The first one is a QoE model based on Bayesian regularized neural networks (BRNN) for multimedia traffic classification based on the objective QoE. A second model determines which video characteristics should be changed to improve QoE in difficult situations. These characteristics are selected based on QoS parameters. [PERSON] et al. [22] proposed a QoS-enabled management framework to support the transmission of video streaming and multimedia applications over SDN networks. They classified traffic as a QoS flow or a best effort flow. The framework used either an algorithm that optimized routes or a queueing mechanism to guarantee the service requirements. The routing algorithm dynamically rerouted the high priority flow when network congestion occurs; if there is no feasible path to transmit the QoS flow, the framework enabled a queue reservation instead. [PERSON] and [PERSON] [23] designed a video over software-defined networking (VSDN) architecture and protocol for optimizing QoS routing and queuing for video transmutation. They implemented a signaling QoS framework like integrated services (IntServ) to guarantee end-to-end QoS for video applications. With the VSDN protocol, video applications can request video service from the network by providing a QoS API used by sender and receiver. Despite that, the architecture may not be scalable in large networks due to the potential signaling overhead between video senders and receivers. Finally, [PERSON] et al. [24] presented the HiQoS SDN framework to guarantee QoS. It provided services differentiation and multipath routing. HiQoS computed multiple paths between the source and destination using a modified Dijkstra algorithm; the optimal ones were chosen based on the lowest bandwidth consumption. As a result, it was resilient against link failures through rerouting flow to another route. However, HiQoS had only used minimal bandwidth utilization as a metric, ignoring other video streaming quality metrics. Our proposed VQoSRR, instead, employs queue mechanisms to meet bandwidth guarantees for video traffic in addition to providing two routing paths between the source and destination to satisfy multiple QoE constraints. According to the previously mentioned related works, many of them are focused on per-flow QoS routing, for example, [8; 9; 10; 11; 12; 13]. In contrast, others focused on resource reservation schemes only, such as [16; 17; 18; 19]. Other approaches used both methods interchangeably [22] or together [23; 24]. Using QoS-based routing only determines the path with the best chance of acquiring the requested QoS.