Abstract: In real-time audio and video transmission, QoS (Quality of Service) metrics reflect the perceived network conditions at the server side, while QoE (Quality of Experience) metrics directly embody the satisfaction level of users with video services. Although QoE metrics are of greater concern to service providers, cloud service providers often cannot obtain QoE data in real-time due to issues such as interface adaptation and user privacy protection, making it difficult to predict and optimize potential QoE anomalies in a timely manner. Given the existing mapping relationship between QoS and QoE, this paper proposed a model that utilizes server-side QoS metrics to detect bottlenecks in QoE metrics, aiming to reduce the workload of operation and maintenance personnel and improve network optimization efficiency. The model employs an imbalanced decision tree for QoS-QoE prediction to achieve QoE anomaly detection. Furthermore, an LSTM regression model is utilized for causal analysis to locate bottlenecks. Experiments show that this model achieves high accuracy in QoE anomaly detection and can identify QoS metrics that significantly impact transmission outcomes.

Key words: QoS-QoE, anomaly detection, causal analysis, real-time communication