## 2 Related Work
Authors in [24] proposed an intelligent QoS management framework for video traffic over SDN named LearnQoS. Their framework uses RL to improve the operation of a policy-based network management to guarantee the compliance of QoS demands for multimedia traffic over SDN. Besides, the work in [8] presented an adaptive approach for controlling multimedia traffic flow in an SDN-enabled network with a deep reinforcement learning method. Their system can realise the flow control policy directly from experience and assign bandwidth in order to maximise its total reward represented by customers' QoE. Another technique to utilise RL has been presented in [25] to satisfy the QoS demands. They proposed an RL-driven QoS-aware routing algorithm. Authors consider a QoS monitoring module to compute delay and packet-loss ratio and RL-based intelligent routing decision-making (RIRD) to interact with the monitoring system and obtain the best path. When the RL agent chooses the route with the minimum delay and packet-loss ratio during implementation, it should receive the highest reward value. Authors in [26] employed a Q-learning as a congestion-aware routing protocol over SDN called (QCAR). They utilised a set of parameters to denote the node and link status, node queue length, the hop count to the destination and re-transmitted packet rate. These parameters were measured periodically and used by the RL agent to calculate the Q-value in order to obtain the best path by avoiding congestion. The results indicate that QCAR performed better than the existing methods and showed an improvement of more than 15%. [PERSON] et al. [27; 28] presented a new RL-based framework that determines the most suitable algorithm from a set of state-of-the-art routing algorithms to be used on the QoS-based traffic flows for the sake of QoS provisioning improvement. [PERSON] et al. [29] focuses on traffic engineering in hybrid SDNs. It presents the RL method to achieve link load balancing while avoiding routing iterations by reactingto the dynamic change in network traffic. Moreover, in [30], authors employed a deep reinforcement learning (DRL) technique for routing optimisation over SDN. The network topology operated in the data plane was data-centre networks. The scheme integrates various network resources, including bandwidth and cache memory, in order to find their unified contribution to minimising delay. The resulted information was then utilised to enhance the routing performance. In summary, several existing literature approaches focus on taking advantage of the integration of ML and SDN to achieve better network QoS. However, only few prior studies [8; 24; 27; 28] considered the improvement of end-users' QoE via route-based learning. This paper aims to address this issue by introducing a framework that realises improving the satisfaction degree of end-users towards multimedia services by utilising SDN characteristics with RL, which intelligently decides the best path for multimedia flows. The proposed scheme follows the approach presented in [26], in terms of directly learning the route hop-by-hop. It also considers four link-state metrics (i.e., available bandwidth, delay, jitter and packet loss rate) as parameters for the RL agent to deliver video streaming packets. The proposed RL-based solution learns the next route, hop-by-hop, for video packets at each OpenFlow switch from the source to the destination while prioritising links' available bandwidth and avoiding links with high delay, jitter and packet loss rate.