STQ-Scheduler: A Secure and Throughput-Aware Deep Reinforcement Learning Framework for QoE-Driven Resource Scheduling in Distributed Video Streaming Systems

Abstract

. With the rapid growth of large-scale video streaming services, cloud gaming, and edge-assisted media delivery, ensuring high Quality of Experience (QoE) under dynamic network conditions and heterogeneous edge infrastructures has become a critical challenge. In practical systems, issues such as burst traffic, cross-edge latency variability, and noisy or unsafe data streams often lead to suboptimal resource utilization and degraded user experience. Meanwhile, conventional rule-based schedulers and static data processing pipelines are unable to jointly address the challenges of efficient model training and adaptive resource allocation. To tackle these problems, this paper proposes STQ-Scheduler, a secure and throughput-aware deep reinforcement learning framework that integrates high-throughput data processing, Transformer-based QoE prediction, and Proximal Policy Optimization (PPO)-based resource scheduling. The framework incorporates real-time data cleaning, anomaly filtering, and online feature transformation to ensure data quality and prevent data processing from becoming a bottleneck in distributed training. Furthermore, a multi-objective reward function is designed to jointly optimize QoE, latency, throughput, and system cost, enabling adaptive scheduling decisions across distributed edge nodes. Experimental results demonstrate that STQ-Scheduler significantly outperforms baseline methods in distributed video streaming environments. Specifically, it reduces average latency from 215 ms to 162 ms and improves throughput from 910 req/s to 1075 req/s under normal workloads. Under burst conditions, it maintains over 1020 req/s while reducing SLA violation rates from 10.5% to 3.9%, confirming its robustness and effectiveness in QoE-driven resource scheduling.