Hybrid Feedback-Guided Optimal Learning for Wireless Interactive Panoramic Scene Delivery

2026-02-06

AI Summary

This work addresses the challenge of low-latency, high-frame-rate viewport streaming under bandwidth constraints in wireless immersive applications. To this end, the authors propose a two-stage hybrid feedback online learning framework that leverages the observability of user head poses to obtain full-information feedback during the prediction stage and bandit feedback during the transmission stage. Building upon this framework, they introduce AdaPort, the first adaptive algorithm tailored to this setting, and establish instance-dependent regret bounds. Theoretical analysis demonstrates that the upper regret bound of AdaPort asymptotically matches the derived lower bound, confirming its near-optimality. Extensive simulations further validate that AdaPort significantly outperforms existing baseline methods in practical scenarios.

Technology Category

• Machine Learning: Online Learning & Bandits
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Immersive applications such as virtual and augmented reality impose stringent requirements on frame rate, latency, and synchronization between physical and virtual environments. To meet these requirements, an edge server must render panoramic content, predict user head motion, and transmit a portion of the scene that is large enough to cover the user viewport while remaining within wireless bandwidth constraints. Each portion produces two feedback signals: prediction feedback, indicating whether the selected portion covers the actual viewport, and transmission feedback, indicating whether the corresponding packets are successfully delivered. Prior work models this problem as a multi-armed bandit with two-level bandit feedback, but fails to exploit the fact that prediction feedback can be retrospectively computed for all candidate portions once the user head pose is observed. As a result, prediction feedback constitutes full-information feedback rather than bandit feedback. Motivated by this observation, we introduce a two-level hybrid feedback model that combines full-information and bandit feedback, and formulate the portion selection problem as an online learning task under this setting. We derive an instance-dependent regret lower bound for the hybrid feedback model and propose AdaPort, a hybrid learning algorithm that leverages both feedback types to improve learning efficiency. We further establish an instance-dependent regret upper bound that matches the lower bound asymptotically, and demonstrate through real-world trace driven simulations that AdaPort consistently outperforms state-of-the-art baseline methods.

Problem

• panoramic scene delivery
• hybrid feedback
• online learning
• viewport prediction
• wireless transmission

Innovation

• hybrid feedback
• online learning
• viewport prediction
• AdaPort
• regret bound