  Throughput Prediction is one of the primary preconditions for the
uninterrupted operation of several network-aware mobile applications, namely
video streaming. Recent works have advocated using Machine Learning (ML) and
Deep Learning (DL) for cellular network throughput prediction. In contrast,
this work has proposed a low computationally complex simple solution which
models the future throughput as a multiple linear regression of several present
network parameters and present throughput. It then feeds the variance of
prediction error and measurement error, which is inherent in any measurement
setup but unaccounted for in existing works, to a Kalman filter-based
prediction-correction approach to obtain the optimal estimates of the future
throughput. Extensive experiments across seven publicly available 5G throughput
datasets for different prediction window lengths have shown that the proposed
method outperforms the baseline ML and DL algorithms by delivering more
accurate results within a shorter timeframe for inferencing and retraining.
Furthermore, in comparison to its ML and DL counterparts, the proposed
throughput prediction method is also found to deliver higher QoE to both
streaming and live video users when used in conjunction with popular Model
Predictive Control (MPC) based adaptive bitrate streaming algorithms.
