Cumulative Quality Modeling for HTTP Adaptive Streaming

TL;DR

This paper introduces a simple yet effective model to estimate the cumulative visual quality of HTTP Adaptive Streaming sessions, accounting for quality fluctuations over time, and demonstrates its high prediction accuracy and real-time applicability.

Contribution

The paper presents a novel cumulative quality model based on statistical analysis of video segments, improving prediction accuracy and deployment simplicity over existing models.

Findings

The model achieves high prediction performance.

It outperforms related quality models.

It is suitable for real-time estimation.

Abstract

Thanks to the abundance of Web platforms and broadband connections, HTTP Adaptive Streaming has become the de facto choice for multimedia delivery nowadays. However, the visual quality of adaptive video streaming may fluctuate strongly during a session due to bandwidth fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this paper, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify three important components of the cumulative quality model, namely the minimum window quality, the last window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. The source code of the proposed model has been made available to the public at https://github.com/TranHuyen1191/CQM.