Huyen T. T. Tran
ABSTRACT
HTTP Adaptive Streaming (HAS) has become a popular solution for multimedia delivery nowadays. However, because of throughput fluctuations, video quality may be dramatically varying. Also, stalling events may occur during a streaming session, causing negative impacts on user experience. Therefore, a main challenge in HAS is how to evaluate the overall quality of a session taking into account the impacts of quality variations and stalling events. In this paper, we present an open software, called BiQPS, using a Long-Short Term Memory (LSTM) network to predict the overall quality of HAS sessions. The prediction is based on bitstream-level parameters, so it can be directly applied in practice. Through experiment results, it is found that BiQPS outperforms four existing models. Our software has been made available to the public at https://github.com/TranHuyen1191/BiQPS.
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Index Terms
- An open software for bitstream-based quality prediction in adaptive video streaming
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