Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach

Eswara, Nagabhushan and Ashique, S. and Panchbhai, Anand and Chakraborty, Soumen and Sethuram, Hemanth P. and Kuchi, Kiran and Kumar, Abhinav and Channappayya, Sumohana S. (2020) Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach. IEEE Transactions on Circuits and Systems for Video Technology, 30 (3). pp. 661-673. ISSN 1051-8215

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Due to the rate adaptation in hypertext transfer protocol adaptive streaming, the video quality delivered to the client keeps varying with time depending on the end-to-end network conditions. Moreover, the varying network conditions could also lead to the video client running out of the playback content resulting in rebuffering events. These factors affect the user satisfaction and cause degradation of the user quality of experience (QoE). Hence, it is important to quantify the perceptual QoE of the streaming video users and to monitor the same in a continuous manner so that the QoE degradation can be minimized. However, the continuous evaluation of QoE is challenging as it is determined by complex dynamic interactions among the QoE influencing factors. Toward this end, we present long short-term memory (LSTM)-QoE, a recurrent neural network-based QoE prediction model using an LSTM network. The LSTM-QoE is a network of cascaded LSTM blocks to capture the nonlinearities and the complex temporal dependencies involved in the time-varying QoE. Based on an evaluation over several publicly available continuous QoE datasets, we demonstrate that the LSTM-QoE has the capability to model the QoE dynamics effectively. We compare the proposed model with the state-of-the-art QoE prediction models and show that it provides an excellent performance across these datasets. Furthermore, we discuss the state space perspective for the LSTM-QoE and show the efficacy of the state space modeling approaches for the QoE prediction.

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IITH Creators:
IITH CreatorsORCiD
Item Type: Article
Uncontrolled Keywords: Adaptive streaming; Quality of experience (QoE); rebuffering; Recurrent neural network (RNN); stalling; Time varying;Brain; Complex networks; Forecasting; HTTP; Hypertext systems; Internet protocols; Quality of service; State space methods; Time varying networks; User experience; Video streaming
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 26 Jul 2021 04:29
Last Modified: 02 Mar 2022 07:10
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