A Reinforcement Learning-Based Routing for Real-Time Multimedia Traffic Transmission over Software-Defined Networking  
[PERSON]  
1 Department of Computing, University of Northampton, Northampton NN1 5 PH, UK 12 Department of Computer Network, College of Information Technology, University of Babylon, Babil 51001, Iraq 21 Department of Computing, University of Northampton, Northampton NN1 5 PH, UK 12 Department of Computer Network, College of Information Technology, University of Babylon, Babil 51  
did not prohibit subscribers from adjusting the streaming resolution, it affected the end-user QoE [1]. Further, inadequate bandwidth increases the delay and loss rate, which leads to decreased end-user QoE. Video streaming is more vulnerable to data loss with high encoding bitrates since they severely impact the video streaming QoE. Users could confront frame freezing, complete video loss, or other problems depending on the lost video frames. On this basis, new and innovative technology solutions are investigated to support the high traffic requirements and provide better QoS and QoE, including Software Defined Networks (SDN) and Network Function Virtualisation (NFV) [3; 4; 5] and Artificial Intelligence (AI) [6; 7; 8].  
SDN is identified as one of the key enabling technologies for modern networks, which provides numerous benefits to improve networks in relation to a logically centralised control model, network programmability, global view and the detachment of the control plane and data plane. The features of the SDN-based environment are suited for the deployment of multimedia bandwidth-hungry applications, such as video streaming [9]. The softwarised networks can be seen as a promising field for enhancing network performance which recently has brought both academic institutions and industry to study further issues in network performance optimisation. Additionally, AI and its subset machine learning (ML) integration has acquired an increasing reputation due to its utilisation in practically every sector [10; 11; 12; 13].  
This article introduces a new approach of Reinforcement Learning (RL)-based multimedia traffic transmission in SDN environment that considers QoS metrics and network information to enable exploring, learning, and exploiting potential paths for video streaming traffic. The proposed approach aims to enhance the satisfaction degree of end-users represented by QoE, towards Dynamic Adaptive Streaming over HTTP (DASH) based video flows leveraging the RL in SDN. It is implemented using emulation with real video content streamed by a multimedia provider and evaluated against other existing solutions, under different realistic SDN-enabled networks in terms of bandwidth, latency, jitter, loss, structural similarity index measure (SSIM) and video multimethod assessment fusion (VMAF).  
The remainder of this article is organised as follows. Section 2 brings up some existing works on adaptive routing for multimedia traffic over SDN-enabled networks. Section 3 illustrates the proposed RL-based multimedia traffic routing framework. Section 4 highlights the problem domain and the proposed RL-based decision making solution. Section 5 presents the evaluation of the proposed approach. Section 6 describes the experiments' results that back our proposed RL-based solution. Finally, conclusion and future work are presented in Section 7.