  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.
