Adaptive 360° video streaming for teleoperation faces dual challenges: viewport prediction under uncertain gaze patterns and bitrate adaptation over volatile wireless channels. While data-driven and Deep Reinforcement Learning (DRL) methods achieve high Quality of Experience (QoE), their "black-box" nature and reliance on training data can limit deployment in safety-critical systems. To address this, we propose OrbitStream, a training-free framework that combines semantic scene understanding with robust control theory. We formulate viewport prediction as a Gravitational Viewport Prediction (GVP) problem, where semantic objects generate potential fields that attract user gaze. Furthermore, we employ a Saturation-Based Proportional-Derivative (PD) Controller for buffer regulation. On object-rich teleoperation traces, OrbitStream achieves a 94.7\% zero-shot viewport prediction accuracy without user-specific profiling, approaching trajectory-extrapolation baselines ($\sim$98.5\%). Across 3,600 Monte Carlo simulations on diverse network traces, OrbitStream yields a mean QoE of 2.71. It ranks second among 12 evaluated algorithms, close to the top-performing BOLA-E (2.80) while outperforming FastMPC (1.84). The system exhibits an average decision latency of 1.01 ms with minimal rebuffering events. By providing competitive QoE with interpretability and zero training overhead, OrbitStream demonstrates that physics-based control, combined with semantic modeling, offers a practical solution for 360° streaming in teleoperation.

翻译：远程操作中自适应360度视频流传输面临双重挑战：在不确定的注视模式下进行视口预测，以及在易变的无线信道上进行码率自适应。尽管数据驱动和深度强化学习方法能实现高体验质量，但其"黑箱"特性及对训练数据的依赖会限制其在安全关键系统中的部署。为此，我们提出OrbitStream这一无训练框架，将语义场景理解与鲁棒控制理论相结合。我们将视口预测形式化为引力视口预测问题，其中语义对象生成吸引用户注视的势场。此外，我们采用基于饱和的PD控制器进行缓冲区调节。在富含物体的远程操作轨迹数据上，OrbitStream无需用户特定建模即实现94.7%的零样本视口预测准确率，接近轨迹外推基线方法（约98.5%）。在多样化网络轨迹的3600次蒙特卡洛仿真中，OrbitStream的平均体验质量为2.71，在12种评估算法中位列第二，接近最优性能的BOLA-E（2.80），同时优于FastMPC（1.84）。该系统平均决策延迟为1.01毫秒，且极少发生缓冲事件。通过提供具有可解释性与零训练开销的竞争性体验质量，OrbitStream证明了基于物理的鲁棒控制与语义建模相结合，为远程操作中的360度视频流传输提供了实用解决方案。