Video Streaming Over QUIC: A Comprehensive Study

TL;DR

This paper provides a comprehensive analysis of QUIC transport protocol implementations, focusing on their congestion control performance and impact on video streaming quality in various network scenarios.

Contribution

It offers the first extensive comparison of different QUIC congestion control algorithms and their effects on adaptive streaming performance and user QoE.

Findings

Different QUIC CCs cause significant performance variations.

Identical CC algorithms across implementations impact QoE differently.

Cross-layer design can optimize streaming performance.

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 transport-layer congestion control (CC) performance and its impact on HTTP Adaptive Streaming (HAS) 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.