DeepQoE: A Multimodal Learning Framework for Video Quality of Experience (QoE) Prediction

Zhang, Huaizheng; Dong, Linsen; Gao, Guanyu; Hu, Han; Wen, Yonggang; Guan, Kyle

doi:10.1109/tmm.2020.2973828

Cited by 53 publications

(25 citation statements)

References 45 publications

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“…The CNN-QoE utilizes the benefits of TCN to overcome the computational complexity limitations of LSTM-based QoE models, whilst also providing architectural enhancements to increase QoE prediction accuracy. In [148], DeepQoE (an end-to-end framework for QoE estimation) is presented. DeepQoE is based on a combination of DL techniques, including word embedding, 3D CNN and representation learning.…”

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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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%

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

et al. 2022

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“…3) Reinforcement Learning: Reinforcement learning has been applied in many areas for intelligent control, e.g., autonomous vehicle, video streaming [21], [22], resource provisioning [23], etc. It is concerned with how the agent should take actions in a dynamic environment to maximize the overall rewards [24].…”

Section: A Preliminary 1) Hvacmentioning

confidence: 99%

DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning

Gao

Wen

2020

IEEE Internet Things J.

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185

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Heating, Ventilation, and Air Conditioning (HVAC) are extremely energy-consuming, accounting for 40% of total building energy consumption. It is crucial to design some energyefficient building thermal comfort control strategy which can reduce the energy consumption of the HVAC while maintaining the comfort of the occupants. However, implementing such a strategy is challenging, because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep reinforcement learning based framework, DeepComfort, for thermal comfort control in buildings. We formulate the thermal comfort control as a cost-minimization problem by jointly considering the energy consumption of the HVAC and the occupants' thermal comfort. We first design a deep Feedforward Neural Network (FNN) based approach for predicting the occupants' thermal comfort, and then propose a Deep Deterministic Policy Gradients (DDPG) based approach for learning the optimal thermal comfort control policy. We implement a building thermal comfort control simulation environment and evaluate the performance under various settings. The experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants' thermal comfort by 13.6%.

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“…In the video delivery, Quality of Experience (QoE) is generally adopted to measure the performance 6 . Inspired by QoE oriented video streaming, we propose the user experience driven intelligent translation scheme, which takes both the translation quality and response latency into account.…”

Section: Design Of Mec‐transmentioning

confidence: 99%

MEC‐based latency aware optical character recognition and realtime English translation for smart cities

Zhang

2020

Internet Technology Letters

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In this paper, we propose the mobile edge computing based latency aware OCR and realtime English translation (MEC‐Trans) for smart cities. MEC‐Trans monitors the local resource status of the edge platform and the network status from the edge to the cloud in real time. Based on this, it adopts an intelligent decision mechanism driven by user experience. It offloads some computation to the edge and dynamically reduces the network transmission data amount. In this way a relatively stable quality of user experience can be guaranteed.

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DeepQoE: A Multimodal Learning Framework for Video Quality of Experience (QoE) Prediction

Cited by 53 publications

References 45 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

DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings Via Reinforcement Learning

MEC‐based latency aware optical character recognition and realtime English translation for smart cities

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