MultiQoE: Measuring QoE of DASH Video from Encrypted Traffic with Multimodal Features

QoE metrics for video provides network operators with insight into the quality of service of their video delivery, giving them valid information to optimize bandwidth resource allocation. However, with the popularization of end-to-end encryption protocols (e.g., SSL/TLS), operators cannot directly obtain valuable information from encrypted traffic. In this paper, we present MultiQoE, which leverages multimodal features with multihead attention mechanism, enabling more accurate and wide-ranging real-time DASH video QoE measurements. We carefully select round-trip time (RTT) and throughput (THR) as multimodal input features so as to capture complementary information. Building on this, we develop a robust deep learning architecture that integrates convolutional neural network for effective feature extraction and multihead attention mechanism for enhanced contextual understanding. This combination allows the model to process complex relationships between the input modalities and deliver more precise measurement related to video QoE metrics. We evaluate MultiQoE on the real-world DASH traffic dataset collected from our platform, and it outperform existing methods in QoE measurement across four tasks. Resolution and rebuffering time classification improve by 2% and 0.54%, while MSE for rebuffering duration and end time decrease by 4.32% and 1.54%, respectively.