Empirical Conditional Method: A New Approach to Predict Throughput in TCP Mobile Data Network

TL;DR

This paper introduces the Empirical Conditional Method (ECM), a new Markov-based prediction algorithm that improves bandwidth forecasting accuracy for live video streaming over mobile networks, enhancing video quality and network utilization.

Contribution

The paper proposes ECM, a novel prediction method utilizing past data more effectively, outperforming the simple Arithmetic Mean in accuracy and bandwidth utilization in mobile network scenarios.

Findings

ECM reduces prediction loss by about 10% compared to AM.

ECM achieves higher bandwidth utilization without increasing loss or delay.

ECM improves video streaming quality under limited network conditions.

Abstract

Experience of live video streaming can be improved if future available bandwidth can be predicted more accurately at the video uploader side. Thus follows a natural question which is how to make predictions both easily and precisely in an ever-changing network. Researchers have developed many prediction algorithms in the literature, from where a simple algorithm, Arithmetic Mean (AM), stands out. Based on that, we are purposing a new method called Empirical Conditional Method (ECM) based on a Markov model, hoping to utilize more information in the past data to get a more accurate prediction without loss of practicality. Through simulations, we found that our ECM algorithm performs better than the commonly used AM one, in the sense of reducing the loss by about 10% compared with AM. Besides, ECM also has a higher utilization rate of available bandwidth, which means ECM can send more data out while not having a higher loss rate or delay, especially under a low FPS setting. ECM can be more helpful for those who have relatively limited networks to reach a more considerable balance between frame loss rate and video quality hence improving the quality of experience.