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.

翻译：吞吐量预测是多种网络感知型移动应用（例如视频流）实现无中断运行的主要前提条件之一。近期研究倡导利用机器学习（ML）和深度学习（DL）进行蜂窝网络吞吐量预测。相比之下，本研究提出了一种低计算复杂度的简单解决方案，该方案将未来吞吐量建模为当前若干网络参数及当前吞吐量的多元线性回归函数；进而将预测误差方差与测量误差方差（该误差固有存在于任何测量设置中，但现有研究均未予考虑）输入至基于卡尔曼滤波的预测-修正方法中，以获得未来吞吐量的最优估计值。针对七种公开可用的5G吞吐量数据集，在不同预测窗口长度下开展的大量实验表明：所提方法通过更短的推理与重新训练时间实现了比基线ML和DL算法更精确的结果。此外，与基于ML和DL的同类方法相比，当该吞吐量预测方法与基于模型预测控制（MPC）的自适应码率流算法结合使用时，能够为流媒体用户和直播视频用户均带来更高的体验质量（QoE）。