TY - JOUR
T1 - Enhancing User Experience in Ultra HD Cloud Performing Arts Live Streaming
T2 - A QoS-to-QoE Mapping Approach
AU - Yang, Li
AU - Liu, Jianzhang
AU - Li, Shufeng
AU - Zhang, Deyou
AU - Xia, Zhiping
N1 - Publisher Copyright:
© 1963-12012 IEEE.
PY - 2024/6/1
Y1 - 2024/6/1
N2 - Video streaming services have gradually become the dominant use case on the Internet, where people's focus has shifted from a service-centric approach to a user-centric approach. This paper proposes a continuous Quality of Experience (QoE) evaluation method based on Quality of Service (QoS) parameters, which combines the advantages of subjective and objective research methods. The proposed method can accurately calculate the achievable QoE based on user QoS. Additionally, we develop a QoE-Ensemble MLP prediction model, employing ensemble learning and MLP techniques, to overcome limitations of the QoE evaluation method and accurately predict user QoE based on QoS parameters. Furthermore, we propose a low-complexity network bandwidth allocation algorithm based on a QoE prediction model to help service providers minimize network bandwidth waste while meeting user QoE requirements. Finally, the experiments show that our QoE evaluation model and network bandwidth allocation algorithm have better performance. And according to the result analysis, we also got the connection between QoS parameters and QoE.
AB - Video streaming services have gradually become the dominant use case on the Internet, where people's focus has shifted from a service-centric approach to a user-centric approach. This paper proposes a continuous Quality of Experience (QoE) evaluation method based on Quality of Service (QoS) parameters, which combines the advantages of subjective and objective research methods. The proposed method can accurately calculate the achievable QoE based on user QoS. Additionally, we develop a QoE-Ensemble MLP prediction model, employing ensemble learning and MLP techniques, to overcome limitations of the QoE evaluation method and accurately predict user QoE based on QoS parameters. Furthermore, we propose a low-complexity network bandwidth allocation algorithm based on a QoE prediction model to help service providers minimize network bandwidth waste while meeting user QoE requirements. Finally, the experiments show that our QoE evaluation model and network bandwidth allocation algorithm have better performance. And according to the result analysis, we also got the connection between QoS parameters and QoE.
KW - QoS-to-QoE mapping
KW - UHD cloud performing arts live streaming
KW - bandwidth resource optimization
KW - ensemble learning
KW - recommendation algorithm
UR - https://www.scopus.com/pages/publications/85186067754
U2 - 10.1109/TBC.2024.3358756
DO - 10.1109/TBC.2024.3358756
M3 - 文章
AN - SCOPUS:85186067754
SN - 0018-9316
VL - 70
SP - 413
EP - 428
JO - IEEE Transactions on Broadcasting
JF - IEEE Transactions on Broadcasting
IS - 2
ER -