Point Cloud Streaming with Latency-Driven Implicit Adaptation using MoQ

2025-07-21

AI Summary

Point cloud video streaming faces significant challenges in virtual/augmented reality due to its high bandwidth requirements and stringent low-latency constraints; existing HTTP-based explicit client-side adaptive bitrate (ABR) mechanisms suffer from response lag and control complexity. This paper proposes an implicit adaptive transmission framework built upon QUIC and Media over QUIC (MoQ). It introduces, for the first time, server-side, latency-driven bitrate adaptation leveraging MoQ’s delivery timeout mechanism—eliminating the need for explicit client feedback. Integrated with point cloud tiling compression and latency-aware scheduling, the framework enables end-to-end low-latency streaming. Experiments demonstrate that the approach automatically optimizes perceptual quality under heterogeneous network conditions while respecting terminal-specific latency budgets: it ensures playback continuity under strict latency constraints and significantly enhances visual fidelity under relaxed ones, thereby achieving personalized latency–quality trade-offs.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Point clouds are a promising video representation for next-generation multimedia experiences in virtual and augmented reality. Point clouds are notoriously high-bitrate, however, which limits the feasibility of live streaming systems. Prior methods have adopted traditional HTTP-based protocols for point cloud streaming, but they rely on explicit client-side adaptation to maintain low latency under congestion. In this work, we leverage the delivery timeout feature within the Media Over QUIC protocol to perform implicit server-side adaptation based on an application's latency target. Through experimentation with several publisher and network configurations, we demonstrate that our system unlocks a unique trade-off on a per-client basis: applications with lower latency requirements will receive lower-quality video, while applications with more relaxed latency requirements will receive higher-quality video.

Problem

• Reducing high-bitrate point cloud streaming challenges
• Enabling implicit server-side adaptation for latency control
• Balancing video quality and latency per-client needs

Innovation

• Implicit server-side adaptation via MoQ
• Leverages delivery timeout for latency targets
• Per-client quality-latency trade-off optimization