Training-Free Adaptive 360-degree Video Streaming via Semantic Potential Fields
Abstract
Adaptive 360{\deg} video streaming for teleoperation faces two coupled challenges: viewport prediction under uncertain gaze patterns and bitrate adaptation over fluctuating wireless channels. While Deep Reinforcement Learning (DRL) methods achieve high Quality of Experience (QoE), their lack of interpretability and dependence on offline training limit deployment in safety-critical systems. We propose OrbitStream, a training-free framework that formulates viewport prediction as a Gravitational Viewport Prediction (GVP) problem, where semantic objects generate potential fields that attract operator gaze, and employs a Saturation-Based Proportional-Derivative (PD) Controller for buffer regulation. On object-rich teleoperation traces, OrbitStream achieves 94.7% zero-shot viewport prediction accuracy without user-specific profiling, approaching trajectory-extrapolation baselines (~98.5%). Across 3,600 Monte Carlo simulations, it ranks second among 12 algorithms (QoE 2.71 vs. BOLA-E's 2.80), outperforming FastMPC (1.84), with 1.01 ms decision latency and minimal rebuffering.
Cite
@article{arxiv.2603.20999,
title = {Training-Free Adaptive 360-degree Video Streaming via Semantic Potential Fields},
author = {Aizierjiang Aiersilan and Zhangfei Yang},
journal= {arXiv preprint arXiv:2603.20999},
year = {2026}
}
Comments
We are pleased to announce that this paper has been accepted by the 35th International Conference on Computer Communications and Networks (ICCCN 2026). We appreciate the valuable feedback from the reviewers and look forward to sharing our findings with the community