RMT: Real-Time Multi-Level Transformer for Detecting Downgrades of User Experience in Live Streams

Live-streaming platforms such as TikTok have been recently experiencing exponential growth, attracting millions of daily viewers. This surge in network traffic often results in increased latency, even on resource-rich nodes during peak times, leading to the downgrade of Quality of Experience (QoE) for users. This study aims to predict QoE downgrade events by leveraging cross-layer device data through real-time predictions and monitoring. We propose a Real-time Multi-level Transformer (RMT) model to predict the QoE of live streaming by integrating time-series data from multiple network layers. Unlike existing approaches, which primarily assess the immediate impact of network conditions on video quality, our method introduces a device-mask pretraining (DMP) technique that applies pretraining on cross-layer device data to capture the correlations among devices, thereby improving the accuracy of QoE predictions. To facilitate the training of RMT, we further built a Live Stream Quality of Experience (LSQE) dataset by collecting 5,000,000 records from over 300,000 users in a 7-day period. By analyzing the temporal evolution of network conditions in real-time, the RMT model provides more accurate predictions of user experience. The experimental results demonstrate that the proposed pretraining task significantly enhances the model’s prediction accuracy, and the overall method outperforms baseline approaches.