Bidirectional LSTM for Continuously Predicting QoE in HTTP Adaptive Streaming

Duc, Tho Nguyen; Tran, Chanh Minh; Tan, Phan Xuan; Kamioka, Eiji

doi:10.1145/3322645.3322687

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…In the case of HAS video, the prediction models examine IFs that derive from end-user's traffic pattern characteristics [136] and incorporate both forward and backward dependence of the continuous QoE prediction [137]. The most accurate model however [138], is an end-to-end and unified predictive approach based on deep learning (DL) as a mix of CNN and LSTM that uses the MOS metric to assess QoE.…”

Section: A Video Streamingmentioning

confidence: 99%

“…The most accurate model however [138], is an end-to-end and unified predictive approach based on deep learning (DL) as a mix of CNN and LSTM that uses the MOS metric to assess QoE. In [136], the predictive model utilizes three distinct component selection methods and six different classifiers, by employing SL techniques, whereas, in [137], the inputs from perceptual visual quality metrics, rebuffering, and temporal memory-related data are analyzed, with use of bidirectional LSTM (BLSTM). As we can observe in the Table XI, both the models that employ ANNs methods attain higher prediction accuracy than the model based on SL algorithms.…”

Section: A Video Streamingmentioning

confidence: 99%

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A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

et al. 2022

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The groundbreaking evolution in mobile and wireless communication networks design in recent years, in combination with the advancement of mobile terminal equipment capabilities, has led in an exponential growth of mobile internet technologies, and arose an ever-growing demand for innovative multimedia services. The highly demanding in terms of network resources over-the-top media services, as well as the emergence of new and complex mobile multimedia services such as video gaming, ultra-highdefinition video, and extended reality, requires the enhancement of end-users' perceived quality of experience (QoE). QoE has garnered much research interest in recent years, and has emerged as a key component in the evaluation of network services and operations. As a result, a QoE-aware network planning approach is getting increasingly favored, and novel design challenges, such as how to quantify and measure QoE, have arisen. In this regard, a paradigm shift in network implementations is being envisioned, in which the focus will be on machine learning (ML) methodologies for developing QoE prediction models, directly related to end-user's personalized experience. In this survey, an analysis on application-oriented, ML-based QoE prediction models for the goal of QoE management for multimedia services is presented. In addition, an examination of the state-of-the-art ML-based QoE predictive models and some of the innovative techniques and challenges related to multimedia services quality assessment with focus on extended reality and video gaming applications are outlined.

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Section: A Video Streamingmentioning

confidence: 99%

Section: A Video Streamingmentioning

confidence: 99%

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

et al. 2022

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“…Therefore, the overall QoE cannot be applied for real-time QoE monitor and also does not give sufficient information about events occurring during the session. Although the instantaneous QoE [6,7,30], on the other hand, can provide the instant perceived video quality at a certain moment, it only reflects locally the quality assessment within a specific time range, without considering the cumulative effects of prior events. Hence, it is highly sensitive to video impairments due to hysteresis effect [18,31] and does not precisely express the user's perceived video quality.…”

Section: Related Workmentioning

confidence: 99%

Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest

et al. 2019

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The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user's cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.

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QoE Models for Online Video Streaming

Zhang

Guo

et al. 2021

2021 20th International Conference on Ubiquitous Computing and Communications (IUCC/CIT/DSCI/SmartCNS)

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Bidirectional LSTM for Continuously Predicting QoE in HTTP Adaptive Streaming

Cited by 5 publications

References 12 publications

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction

Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest

QoE Models for Online Video Streaming

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