QoE QoS Mapping for YouTube Services over LTE Network

Vizzarri, Alessandro; Davide, Fabrizio

doi:10.1145/2989250.2989258

Cited by 3 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, the researchers have mainly focused on QoE prediction in terrestrial networks, such as mobile networks, IoT networks, cloud‐based networks, and cellular networks. In Vizzarri and Davide, the linear QoS/QoE model of over‐the‐top–managed services over LET network was derived. In Sun et al, it was validated that the exponential model is more suitable for QoE/QoS modeling in IoT networks.…”

Section: Related Workmentioning

confidence: 99%

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Wang

Huang

2018

Int J Communication

View full text Add to dashboard Cite

Summary Satellite networks are promising alternatives to deal with the increasing traffic volume and provide universal access for end users. Quality of experience (QoE) as an aggregate of user perception and network quality will be a critical success factor for multimedia service delivery over satellite networks. Quality of experience prediction based on quality of service (QoS) has received much attention recently, but it has been little investigated in satellite networks. In this paper, on the basis of the modular neural network and deep belief networks (DBNs), we design a QoE/QoS mapping method for multimedia services over satellite networks to translate QoS parameters into user QoE. The task of QoE/QoS mapping for multimedia services is decomposed into several different subtasks by using traffic classification. Heterogeneous DBNs are used to learn the mapping relationships between QoE and QoS for different types of services (ie, subtask) simultaneously. An integrative approach based on relative distance is exploited to select subneural network(s) to predict resulting QoE collaboratively. To determine the weight parameters of the QoE/QoS correlation model, the dataset composed of QoS parameters and subjective opinion scores is built on the basis of satellite network simulator and subjective test. Finally, the effectiveness of our QoE/QoS mapping method is validated, and the relationship between adoption rating and opinion scores is analyzed.

show abstract

Section: Related Workmentioning

confidence: 99%

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Wang

Huang

2018

Int J Communication

View full text Add to dashboard Cite

show abstract

Experimental Analysis of ViLTE Service

et al. 2018

View full text Add to dashboard Cite

An Intelligent Handover Mechanism Based on MOS Predictions for Real-Time Video Conference Services in Mobile Networks

2022

View full text Add to dashboard Cite

In mobile networks, handover mechanisms provide fast and smooth access service for mobile users. However, one of the main challenges in mobile networks is the handover management with increased mobility and bandwidth demand of the required network services. Therefore, in this paper, we propose a MOS-aware (mean opinion score-aware) mobile network handover mechanism based on deep learning to determine the appropriate handover time for real-time video conference services in mobile networks. We construct a wireless network topology with LTE characteristics in a Mininet-WiFi simulation. User equipment (UE) can determine the service-required MOS (Mean Opinion Score) from the proposed deep-learning-based handover mechanism with appropriate handover time. Simulation results show that the proposed scheme provides higher performance than the original A3 handover mechanism. The contribution of this paper is to combine the real-time video conferencing services with a deep-learning-based handover mechanism by predicting MOS values to improve the quality of service for users in mobile networks.

show abstract

QoE QoS Mapping for YouTube Services over LTE Network

Cited by 3 publications

References 10 publications

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Modular and deep QoE/QoS mapping for multimedia services over satellite networks

Experimental Analysis of ViLTE Service

An Intelligent Handover Mechanism Based on MOS Predictions for Real-Time Video Conference Services in Mobile Networks

Contact Info

Product

Resources

About