ResLearn: Transformer-based Residual Learning for Metaverse Network Traffic Prediction
This provides Internet service providers with tools for real-time network management to enhance QoS and user experience in the Metaverse, but it is incremental as it builds on existing Transformer and error-learning techniques.
The paper tackles the problem of predicting Metaverse network traffic for XR services by proposing ResLearn, a Transformer-based method that improves time-series predictions, outperforming prior work by 99%.
Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-world dataset of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, made openly available for further research. To enhance prediction accuracy, we then propose a novel view-frame (VF) algorithm that accurately identifies video frames from traffic while ensuring privacy compliance, and we develop a Transformer-based progressive error-learning algorithm, referred to as ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by using fully connected neural networks to reduce errors, particularly during peak traffic, outperforming prior work by 99%. Our contributions offer Internet service providers (ISPs) robust tools for real-time network management to satisfy Quality of Service (QoS) and enhance user experience in the Metaverse.