Autoformer-based mobility and handoff-aware prediction for QoE enhancement in adaptive video streaming in 4G/5G networks

Traditional Adaptive Bitrate (ABR) algorithms in Dynamic Adaptive Streaming over HTTP (DASH) rely on basic throughput estimation techniques that often struggle to quickly adapt to network fluctuations. As users move across different transportation modes or change from one access point to another (e.g., Wi-Fi to cellular networks or between 4G/5G cells), available bandwidth can vary sharply, causing interruptions, abrupt quality shifts, which impact the ability of conventional ABR algorithms to provide seamless playback and maintain high quality-of-experience (QoE). To address these issues, this paper introduces a novel and comprehensive framework that significantly enhances the adaptability and intelligence of ABR algorithms. The proposed solution integrates three key components: a transformer-based throughput prediction model, a Mobility-Aware Throughput Prediction engine (MATH-P), and a Handoff-Aware Throughput Prediction engine (HATH-P). The transformer-based model outperforms state-of-the-art approaches in predicting throughput for both 4G and 5G networks, leveraging its ability to capture complex temporal patterns and long-term dependencies. The MATH-P engine adapts throughput predictions to varying mobility scenarios, while the HATH-P one manages seamless transitions by accurately predicting 4G/5G handoff events and selecting the appropriate throughput prediction model. The proposed systems were integrated into existing ABR algorithms, replacing traditional throughput estimation techniques. Experimental results demonstrate that the MATH-P and HATH-P engines significantly improve video streaming performance, reducing stall durations, enhancing video quality, and ensuring smoother playback.