Advanced search
1 file | 3.01 MB

A review of predictive quality of experience management in video streaming services

Author
Organization
Abstract
Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but also to exert real-time control, addressing the user's quality of experience (QoE) expectations. QoE management is an end-to-end process that, due to the ever-increasing variety of video services, has become too complex for conventional "reactive" techniques. Herein, we review the most significant "predictive" QoE management methods for video streaming services, showing how different machine learning approaches may be used to perform proactive control. We pinpoint a selection of the best suited machine learning methods, highlighting advantages and limitations in specific service conditions. The review leads to lessons learned and guidelines to better address QoE requirements in complex video services.
Keywords
RANDOM NEURAL-NETWORKS, VARIABLE SELECTION, QOE MANAGEMENT, RECOGNITION, MODEL, Machine learning, quality of experience management, video streaming, services

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 3.01 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Torres Vega, Maria, Cristian Perra, Filip De Turck, and Antonio Liotta. 2018. “A Review of Predictive Quality of Experience Management in Video Streaming Services.” Ieee Transactions on Broadcasting 64 (2): 432–445.
APA
Torres Vega, M., Perra, C., De Turck, F., & Liotta, A. (2018). A review of predictive quality of experience management in video streaming services. IEEE TRANSACTIONS ON BROADCASTING, 64(2), 432–445.
Vancouver
1.
Torres Vega M, Perra C, De Turck F, Liotta A. A review of predictive quality of experience management in video streaming services. IEEE TRANSACTIONS ON BROADCASTING. Piscataway: Ieee-inst Electrical Electronics Engineers Inc; 2018;64(2):432–45.
MLA
Torres Vega, Maria, Cristian Perra, Filip De Turck, et al. “A Review of Predictive Quality of Experience Management in Video Streaming Services.” IEEE TRANSACTIONS ON BROADCASTING 64.2 (2018): 432–445. Print.
@article{8567104,
  abstract     = {Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but also to exert real-time control, addressing the user's quality of experience (QoE) expectations. QoE management is an end-to-end process that, due to the ever-increasing variety of video services, has become too complex for conventional {\textacutedbl}reactive{\textacutedbl} techniques. Herein, we review the most significant {\textacutedbl}predictive{\textacutedbl} QoE management methods for video streaming services, showing how different machine learning approaches may be used to perform proactive control. We pinpoint a selection of the best suited machine learning methods, highlighting advantages and limitations in specific service conditions. The review leads to lessons learned and guidelines to better address QoE requirements in complex video services.},
  author       = {Torres Vega, Maria and Perra, Cristian and De Turck, Filip and Liotta, Antonio},
  issn         = {0018-9316},
  journal      = {IEEE TRANSACTIONS ON BROADCASTING},
  keyword      = {RANDOM NEURAL-NETWORKS,VARIABLE SELECTION,QOE MANAGEMENT,RECOGNITION,MODEL,Machine learning,quality of experience management,video streaming,services},
  language     = {eng},
  number       = {2},
  pages        = {432--445},
  publisher    = {Ieee-inst Electrical Electronics Engineers Inc},
  title        = {A review of predictive quality of experience management in video streaming services},
  url          = {http://dx.doi.org/10.1109/TBC.2018.2822869},
  volume       = {64},
  year         = {2018},
}

Altmetric
View in Altmetric
Web of Science
Times cited: