Uncertainty-Aware Neural Network-Based Adaptive Bitrate Streaming over LTE and 5G NR

Adaptive bitrate (ABR) streaming over 4G LTE and 5G New Radio (NR) must cope with rapid, nonlinear throughput variations caused by LTE handovers and 5G beam-management events. Conventional prediction-based ABR schemes often rely on smoothing or history-based throughput predictors, which can exhibit prediction lag under sudden state changes. This paper presents NeUA, an uncertainty-aware ABR framework for mobile networks with transient throughput disruptions. NeUA employs a bidirectional LSTM throughput predictor with Monte Carlo Dropout to quantify epistemic uncertainty in future throughput estimates, allowing the controller to increase the safety margin when predictions are unreliable and reduce it when predictions are stable. NeUA further integrates a single-step model predictive control layer and counter-based hysteresis to reduce unnecessary bitrate oscillations. Evaluations using publicly available LTE and 5G traces show that NeUA achieves the highest QoE among the evaluated prediction-based ABR schemes, improving QoE by up to 8.0% and reducing bitrate-switching frequency by up to 28.8% over the evaluated baselines.