RL-SDNTE: Reinforcement Learning-Driven Traffic Engineering in SDN for QoE Optimization in Video Streaming
TL;DR
RL-SDNTE is presented, a Reinforcement Learning-based TE framework built directly into an SDN controller that targets end-user Quality of Experience (QoE) as its primary objective and scales to topologies beyond 100 nodes without exceeding operationally acceptable convergence times.
Abstract
Video streaming now accounts for over 80% of global Internet bandwidth, yet most SDN traffic engineering (TE) solutions still optimize for throughput and link utilization rather than what users actually experience. Poor startup times, frequent re-buffering, and unstable bit-rate remain common even on well-managed networks -- largely because the control plane has no visibility into application-layer quality. We present RL-SDNTE, a Reinforcement Learning-based TE framework built directly into an SDN controller that targets end-user Quality of Experience (QoE) as its primary objective. Rather than relying on a single proxy metric, RL-SDNTE feeds four perceptual indicators -- startup latency, re-buffering ratio, mean video quality, and bit-rate oscillation -- into a unified reward function that drives routing decisions. A Deep Q-Network (DQN) agent uses the controller’s global network view together with real-time client feedback to continuously adjust path selection. Testing on a Mini-net emulation platform and a physical 12-node SDN test-bed showed gains of up to 34% in composite QoE, 28% fewer re-buffering events, 22% lower startup latency, and 17% less quality oscillation compared to ECMP, OSPF, DEFO, and heuristic QoE- aware baselines [5]-[7],[15]. The system also scales to topologies beyond 100 nodes without exceeding operationally acceptable convergence times, making it viable for real-world SDN deployments.