TY - JOUR
T1 - A Survey on Multimedia Services QoE Assessment and Machine Learning-Based Prediction
AU - Kougioumtzidis, Georgios
AU - Poulkov, Vladimir
AU - Zaharis, Zaharias
AU - Lazaridis, Pavlos
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022/2/24
Y1 - 2022/2/24
N2 - 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-high-definition 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.
AB - 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-high-definition 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.
KW - Extended reality
KW - machine learning
KW - Measurement
KW - multimedia services
KW - Predictive models
KW - QoE prediction models
KW - Quality of experience
KW - quality of experience (QoE)
KW - Quality of service
KW - Solid modeling
KW - Streaming media
KW - video gaming
KW - video streaming
KW - virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85124729287&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3149592
DO - 10.1109/ACCESS.2022.3149592
M3 - Article
AN - SCOPUS:85124729287
VL - 10
SP - 19507
EP - 19538
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9706206
ER -