As a low-latency real-time communication technology, WebRTC is widely used in real-time applications. However, its default congestion control algorithm, GCC, struggles to handle complex network fluctuations, responding slowly to bandwidth changes and adjusting the bitrate inefficiently, which limits high-quality communication. Many improved algorithms focus on increasing bitrate but overlook other factors that affect users’ Quality of Experience (QoE), such as latency and frame rate. To address this issue, we propose LST M and R einforcement Learning for C ongestion C ontrol, which optimizes throughput while jointly considering latency and frame rate. MRCC consists of a Throughput Trend Predictor that forecasts bandwidth variations and a Learning-Based Bitrate Adjuster that dynamically modifies the bitrate based on historical network conditions. This design enhances adaptability, reduces transmission delays, stabilizes frame rates, and improves overall QoE, providing an optimized solution for real-time communication scenarios.