Delay-Constrained Rate Control for Real-Time Video Streaming with Bounded Neural Network

TL;DR

This paper introduces a delay-constrained rate control method for real-time video streaming using deep learning to predict bit rate ranges, improving bandwidth utilization and QoE under dynamic network conditions.

Contribution

It proposes a novel deep learning model that predicts future bit rate ranges based on delay gradients, addressing rapid network fluctuations more effectively than single-value predictions.

Findings

Outperforms state-of-the-art methods in bandwidth utilization.

Achieves 19% to 35% average QoE improvement across scenarios.

Automatically learns throughput-delay relationships from real-world data.

Abstract

Rate control is widely adopted during video streaming to provide both high video qualities and low latency under various network conditions. However, despite that many work have been proposed, they fail to tackle one major problem: previous methods determine a future transmission rate as a single for value which will be used in an entire time-slot, while real-world network conditions, unlike lab setup, often suffer from rapid and stochastic changes, resulting in the failures of predictions. In this paper, we propose a delay-constrained rate control approach based on end-to-end deep learning. The proposed model predicts future bit rate not as a single value, but as possible bit rate ranges using target delay gradient, with which the transmission delay is guaranteed. We collect a large scale of real-world live streaming data to train our model, and as a result, it automatically learns the correlation between throughput and target delay gradient. We build a testbed to evaluate our approach. Compared with the state-of-the-art methods, our approach demonstrates a better performance in bandwidth utilization. In all considered scenarios, a range based rate control approach outperforms the one without range by 19% to 35% in average QoE improvement.