Adaptive 360{\deg} 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{\deg} streaming in teleoperation.

In this paper, we explore a joint source and reconfigurable intelligent surface (RIS)-assisted channel encoding (JSRE) framework for multi-user semantic communications, where a deep neural network (DNN) extracts semantic features for all users and the RIS provides channel orthogonality, enabling a unified semantic encoding-decoding design. We aim to maximize the overall energy efficiency of semantic communications across all users by jointly optimizing the user scheduling, the RIS's phase shifts, and the semantic compression ratio. Although this joint optimization problem can be addressed using conventional deep reinforcement learning (DRL) methods, evaluating semantic similarity typically relies on extensive real environment interactions, which can incur heavy computational overhead during training. To address this challenge, we propose a truncated DRL (T-DRL) framework, where a DNN-based semantic similarity estimator is developed to rapidly estimate the similarity score. Moreover, the user scheduling strategy is tightly coupled with the semantic model configuration. To exploit this relationship, we further propose a semantic model caching mechanism that stores and reuses fine-tuned semantic models corresponding to different scheduling decisions. A Transformer-based actor network is employed within the DRL framework to dynamically generate action space conditioned on the current caching state. This avoids redundant retraining and further accelerates the convergence of the learning process. Numerical results demonstrate that the proposed JSRE framework significantly improves the system energy efficiency compared with the baseline methods. By training fewer semantic models, the proposed T-DRL framework significantly enhances the learning efficiency.