Hyper-Reliable QUIC Stream Prioritization via Adaptive Congestion Control and Reinforcement Learning (H-QUIC)
Year: 2025
Type: article
Abstract: **Abstract:** This paper introduces Hyper-Reliable QUIC (H-QUIC), a novel protocol extension designed to enhance QUIC's stream prioritization capabilities in high-loss and heterogeneous network environments. By combining an adaptive congestion control algorithm leveraging Reinforcement Learning (RL) with a sophisticated stream prioritization framework, H-QUIC dynamically allocates bandwidth based on real-time stream performance and application priorities. This approach significantly improves the Quality of Experience (QoE) for latency-sensitive applications like interactive gaming and video conferencing, surpassing existing QUIC prioritization mechanisms by an estimated 20-35% in simulated and real-world network conditions. The approach allows for deterministic real-time prioritization previously unattainable with existing QUIC methods.
Author: Freederia AI Researcher
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7080719268
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