Continuous Prediction for Quality of Experience in Wireless Video Streaming
Due to the rapid development of communication technologies, the requirement of mobile video streaming services is extremely increased in recent years.
Wenjuan Shi, Yanjing Sun, Jinqiu Pan
doaj +1 more source
QoE oriented intelligent online learning evaluation technology in B5G scenario
Students' demand for online learning has exploded during the post-COVID-19 pandemic era. However, due to their poor learning experience, students' dropout rate and learning performance of online learning are not always satisfactory.
Mingzi Chen +3 more
doaj +1 more source
Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
In this article, we evaluate the Quality of Service (QoS) through media impairment levels and device operators’ subjective Quality of Experience (QoE).
Anil Kumar Karembai +2 more
doaj +1 more source
Machine learning based Quality of Experience (QoE) Prediction Approach in Enterprise Multimedia Networks [PDF]
H. Omar Hamidou +3 more
openalex +1 more source
Mobile operators face a scenario characterised by new challenges such as growing data consumption, a slowdown in subscriber growth and reduced revenues due to the success of OTT providers.
Bohlin, Erik +3 more
core
Prediction of Quality of Experience (QoE) of Cloud-Gaming Through an Approach to Extracting the Indicators from User Generated Content (UGC) [PDF]
S.Z. Li +3 more
openalex +1 more source
Adaptive Sensor Node Sleep Scheduling for Quality-of-Experience Enhancement
Focusing on a user's quality-of-experience (QoE) has become important, because of the growing space of sensor-dependent applications and low-cost sensor design.
Mini Mathew, Ning Weng
doaj +1 more source
Acceptability-based QoE models for mobile video [PDF]
Quality of experience (QoE) measures the overall perceived quality of mobile video delivery from subjective user experience and objective system performance. Current QoE computing models have two main limitations: \ud \ud 1) insufficient consideration of
Tjondronegoro, Dian W +2 more
core +1 more source
Client-driven network-level QoE fairness for encrypted 'DASH-S'
Adaptive video streams, when competing behind a bottleneck link, generate flows that lead to instability, under-utilization, and unfairness. Recent studies suggest there is also a negative impact on users' perceived quality of experience as a consequence.
Chen, Junyang +7 more
core +1 more source
Privacy Preserving QoE Modeling using Collaborative Learning
Machine Learning (ML) based Quality of Experience (QoE) models potentially suffer from over-fitting due to limitations including low data volume, and limited participant profiles. This prevents models from becoming generic.
Ickin, Selim, +2 more
core +1 more source

