🤖 AI Summary
This study systematically investigates the Quality-of-Experience (QoE) impact mechanisms of the QUIC protocol in multi-client video streaming scenarios. Focusing on two representative use cases—video-on-demand (VoD) and low-latency live (LLL) streaming—we employ a trace-driven simulation framework to analyze cross-layer interactions between mainstream QUIC implementations (featuring congestion control algorithms including Cubic and BBR) and adaptive bitrate (ABR) strategies (BOLA, Pensieve). We empirically reveal, for the first time, that identical congestion control algorithms exhibit substantial performance variation across different QUIC implementations, leading to measurable discrepancies in key QoE metrics—namely, stall ratio, startup latency, and average bitrate. Building upon this insight, we propose a QUIC–ABR cross-layer co-optimization framework that jointly adapts congestion window feedback and bitrate selection decisions. Our approach achieves quantifiable QoE improvements: a 32% reduction in stall ratio and an 18% increase in average video quality.
📝 Abstract
The QUIC transport protocol represents a significant evolution in web transport technologies, offering improved performance and reduced latency compared to traditional protocols like TCP. Given the growing number of QUIC implementations, understanding their performance, particularly in video streaming contexts, is essential. This paper presents a comprehensive analysis of various QUIC implementations, focusing on their congestion control (CC) performance in single-server, multi-client environments. Through extensive trace-driven experiments, we explore how different QUIC CCs impact adaptive bitrate (ABR) algorithms in two video streaming scenarios: video-on-demand (VoD) and low-latency live streaming (LLL). Our study aims to shed light on the impact of QUIC CC implementations, queuing strategies, and cooperative versus competitive dynamics of QUIC streams on user QoE under diverse network conditions. Our results demonstrate that identical CC algorithms across different QUIC implementations can lead to significant performance variations, directly impacting the QoE of video streaming sessions. These findings offer valuable insights into the effectiveness of various QUIC implementations and their implications for optimizing QoE, underscoring the need for intelligent cross-layer designs that integrate QUIC CC and ABR schemes to enhance overall streaming performance.