在实时音视频传输中，QoS指标反映服务端可感知的网络情况，QoE指标直接体现用户侧对视频业务的满意程度，尽管QoE指标是服务提供商更为关注的指标，但是由于接口适配和用户隐私保护等问题，云服务提供商往往不能实时获得QoE数据，因此无法及时对可能发生的QoE异常进行预测并采取优化措施。由于QoS-QoE存在一定映射关系，提出一种基于服务端的QoS指标实现对QoE指标进行瓶颈检测的模型，可以减少运维人员定位的工作量，提高网络优化效率。模型使用不平衡决策树进行QoS-QoE预测，实现QoE异常检测。使用LSTM回归模型进行因果分析，实现瓶颈定位。实验表明该模型对QoE异常检测有较高准确率，并且可以发掘传输过程中对传输结果影响较大的QoS指标。


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.


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