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Cumulative Quality Modeling for HTTP Adaptive Streaming

Published:16 April 2021Publication History
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Abstract

HTTP Adaptive Streaming has become the de facto choice for multimedia delivery. However, the quality of adaptive video streaming may fluctuate strongly during a session due to throughput fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this article, 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 four important components of the cumulative quality model, namely the minimum window quality, the last window quality, the maximum 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. Our subjective dataset as well as the source code of the proposed model have been made publicly available at https://sites.google.com/site/huyenthithanhtran1191/cqmdatabase.

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      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 1
      February 2021
      392 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3453992
      Issue’s Table of Contents

      Copyright © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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      Association for Computing Machinery

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      Publication History

      • Published: 16 April 2021
      • Accepted: 1 September 2020
      • Received: 1 August 2020
      Published in tomm Volume 17, Issue 1

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