Enhancing Mobile Immersive Streaming Experience via Deadline-Aware Scheduling and Learning-Enhanced Congestion Control

Mobile immersive streaming applications, such as virtual reality (VR) and augmented reality (AR), impose stringent requirements on network transmission to deliver a seamless user experience. However, existing transmission control methods often fall short in dynamic mobile network environments. Traditional transport-layer protocols typically prioritize network-wide Quality of Service (QoS), overlooking the strict deadlines and varying priorities intrinsic to immersive streaming content, thereby adversely impacting application-specific Quality of Experience (QoE). Furthermore, heuristic-based congestion control methods lack adaptability to rapidly changing network environments, whereas machine learning (ML)-driven approaches, despite their adaptability potential, frequently prove computationally intensive and impractical for deployment on resource-constrained mobile devices. To address these drawbacks, we propose an intelligent transmission control approach that integrates a deadline- and priority-aware scheduler with a learning enhanced congestion control mechanism to optimize immersive streaming content delivery. The scheduling module dynamically prioritizes data packets based on their urgency and importance, timely delivering critical data within strict deadlines as much as possible. Complementing this, the hybrid congestion control module strategically combines lightweight heuristics for baseline performance efficiency with a selectively invoked ML model designed to adaptively adjust the sending rate, efficiently responding to real-time fluctuations in network conditions. Experimental results highlight the effectiveness of our approach, achieving QoE improvements of 15% to 45% compared to existing methods across a range of streaming applications and mobile network scenarios.