Closed-Loop Clustering-Based Global Bandwidth Prediction in Real-Time Video Streaming

Abstract

Accurate throughput forecasting is essential for ensuring the seamless operation of Real-Time Communication (RTC) applications. These demands for accurate throughput forecasting become particularly challenging when dealing with wireless access links, as they inherently exhibit fluctuating bandwidth. Ensuring an exceptional user Quality of Experience (QoE) in this scenario depends on accurately predicting available bandwidth in the short term since it plays a pivotal role in guiding video rate adaptation. Yet, current methodologies for short-term bandwidth prediction (SBP) struggle to perform adequately in dynamically changing real-world network environments and lack generalizability to adapt across varied network conditions. Also, acquiring long and representative traces that capture real-world network complexity is challenging. To overcome these challenges, we propose closed-loop clustering-based Global Forecasting Models (GFMs) for SBP. Unlike local models, GFMs apply the same function to all traces enabling cross-learning, and leveraging relationships among traces to address the performance issues seen in current SBP algorithms. To address potential heterogeneity within the data and improve prediction quality, a clustered-wise GFM is utilized to group similar traces based on prediction accuracy. Finally, the proposed method is validated using real-world datasets of HSDPA 3G, NYC LTE, and Irish 5G data demonstrating significant improvements in accuracy and generalizability.