Massive content delivery will become one of the most prominent tasks of future B5G/6G communication.However,various multimedia applications possess huge differences in terms of object oriented(i.e.,machine or user)and...Massive content delivery will become one of the most prominent tasks of future B5G/6G communication.However,various multimedia applications possess huge differences in terms of object oriented(i.e.,machine or user)and corresponding quality evaluation metric,which will significantly impact the design of encoding or decoding within content delivery strategy.To get over this dilemma,we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision(QoD)or Quality-of-Experience(QoE)for the guidance of content delivery.Then,in terms of machinecentric communication,a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment,which can achieve a balance among decision-making,delivered content,and encoding latency.Finally,in terms of user-centric communication,by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams,we develop a QoE-driven video enhancement scheme to supply high data fidelity.Numerical results demonstrate the remarkable performance improvement of massive content delivery.展开更多
With the rapid growth of the Internet of Things paradigm,a tremendous number of applications and services that require minimal or no human involvement have been developed to enhance the quality of everyday life in var...With the rapid growth of the Internet of Things paradigm,a tremendous number of applications and services that require minimal or no human involvement have been developed to enhance the quality of everyday life in various domains.In order to ensure that such services provide their functionalities with the expected quality,it is essential tomeasure and evaluate this quality,which can be in some cases a challenging task due to the lack of human intervention and feedback.Recently,the vast majority of the Quality of Experience QoE works mainly address the multimedia services.However,the introduction of Internet of Things IoT has brought a new level of complexity into the field of QoE evaluation.With the emerging of the new IoT technologies such as machine to machine communication and artificial intelligence,there is a crucial demand to utilize additional evaluation metrics alongside the traditional subjective and objective human factors and network quality factors.In this systematic review,a comprehensive survey of the QoE evaluation in IoT is presented.It reviews the existing quality of experience definitions,influencing factors,metrics,and models.The review is concluded by identifying the current gaps in the literature and suggested some future research directions accordingly.展开更多
基金partly supported by the National Natural Science Foundation of China (Grants No.62231017 and No.62071254)the Priority Academic Program Development of Jiangsu Higher Education Institutions。
文摘Massive content delivery will become one of the most prominent tasks of future B5G/6G communication.However,various multimedia applications possess huge differences in terms of object oriented(i.e.,machine or user)and corresponding quality evaluation metric,which will significantly impact the design of encoding or decoding within content delivery strategy.To get over this dilemma,we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision(QoD)or Quality-of-Experience(QoE)for the guidance of content delivery.Then,in terms of machinecentric communication,a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment,which can achieve a balance among decision-making,delivered content,and encoding latency.Finally,in terms of user-centric communication,by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams,we develop a QoE-driven video enhancement scheme to supply high data fidelity.Numerical results demonstrate the remarkable performance improvement of massive content delivery.
文摘With the rapid growth of the Internet of Things paradigm,a tremendous number of applications and services that require minimal or no human involvement have been developed to enhance the quality of everyday life in various domains.In order to ensure that such services provide their functionalities with the expected quality,it is essential tomeasure and evaluate this quality,which can be in some cases a challenging task due to the lack of human intervention and feedback.Recently,the vast majority of the Quality of Experience QoE works mainly address the multimedia services.However,the introduction of Internet of Things IoT has brought a new level of complexity into the field of QoE evaluation.With the emerging of the new IoT technologies such as machine to machine communication and artificial intelligence,there is a crucial demand to utilize additional evaluation metrics alongside the traditional subjective and objective human factors and network quality factors.In this systematic review,a comprehensive survey of the QoE evaluation in IoT is presented.It reviews the existing quality of experience definitions,influencing factors,metrics,and models.The review is concluded by identifying the current gaps in the literature and suggested some future research directions accordingly.
