Balancing Generalization and Specialization: Offline Metalearning for Bandwidth Estimation

2024-09-30

arXiv.org

AI Summary

Real-time video applications require accurate bandwidth estimation (BWE) for dynamic adaptive bitrate (ABR) control, yet existing approaches rely on online interaction and struggle with network dynamics and data distribution shifts, limiting quality-of-experience (QoE) gains. To address this, we propose Ivy, an offline meta-learning framework that introduces implicit Q-learning (IQL) to offline BWE meta-learning for the first time. Ivy jointly optimizes generalization and client-specific adaptation by modeling network state representations and enabling dynamic selection among candidate BWE algorithms. Crucially, Ivy performs personalized model adaptation entirely offline—without online interaction—thereby significantly mitigating distributional shift. Evaluated on real-world video conferencing traces, Ivy improves QoE by 5.9–11.2% over the best single BWE algorithm and by 6.3–11.4% over state-of-the-art online meta-heuristic methods.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Metareasoning and Metaheuristics
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

User experience in real-time video applications requires continuously adjusting video encoding bitrates to match available network capacity, which hinges on accurate bandwidth estimation (BWE). However, network heterogeneity prevents a one-size-fits-all solution to BWE, motivating the demand for personalized approaches. Although personalizing BWE algorithms offers benefits such as improved adaptability to individual network conditions, it faces the challenge of data drift -- where estimators degrade over time due to evolving network environments. To address this, we introduce Ivy, a novel method for BWE that leverages offline metalearning to tackle data drift and maximize end-user Quality of Experience (QoE). Our key insight is that dynamically selecting the most suitable BWE algorithm for current network conditions allows for more effective adaption to changing environments. Ivy is trained entirely offline using Implicit Q-learning, enabling it to learn from individual network conditions without a single, live videoconferencing interaction, thereby reducing deployment complexity and making Ivy more practical for real-world personalization. We implemented our method in a popular videoconferencing application and demonstrated that Ivy can enhance QoE by 5.9% to 11.2% over individual BWE algorithms and by 6.3% to 11.4% compared to existing online meta heuristics.

Problem

• Dynamic bandwidth estimation for real-time video applications
• Offline meta-learning to combat data drift in networks
• Adaptive algorithm selection for changing network conditions

Innovation

• Ivy uses offline meta-learning for bandwidth estimation
• It dynamically selects best algorithm for network conditions
• Method improves data efficiency without live interactions