A joint reinforcement learning (RL) and game theory method is presented for segment-level continuous bitrate selection and tile-level bitrate allocation in tile-based 360-degree streaming to increase users' quality of experience (QoE). First, a viewpoint prediction method based on single-user (SU) viewpoint traces and the saliency map (SM) model is presented to model viewing behaviours. Second, an RL method is proposed to predict segment bitrate and a cooperative bargaining game theory is proposed for bitrate allocation optimization to choose a suitable bitrate for every tile with the help of the viewpoint prediction map. Performance evaluation results indicate that the proposed method can outperform the state-of-the-art methods in terms of different QoE objectives.