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A Review of Predictive Quality of Experience Management in Video Streaming Services
- Publication Year :
- 2018
- Publisher :
- Institute of Electrical and Electronics Engineers, 2018.
-
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.
- Subjects :
- Process (engineering)
Computer science
004 Data processing & computer science
QA75 Electronic computers. Computer science
Control (management)
Information science
Internet of Things
Feature selection
02 engineering and technology
computer.software_genre
Machine learning, quality of experience management, video streaming services
Information visualisation
Task (project management)
Automation
0202 electrical engineering, electronic engineering, information engineering
Media Technology
Centre for Distributed Computing, Networking and Security
Quality of experience
Electrical and Electronic Engineering
Software systems
Service (business)
Smart mobility
Multimedia
User experience
Sensors
Quality of service
020206 networking & telecommunications
Centre for Algorithms, Visualisation and Evolving Systems
Variety (cybernetics)
AI and Technologies
Health
020201 artificial intelligence & image processing
eHealth
Networks
computer
Smart cities
Subjects
Details
- Language :
- English
- ISSN :
- 00189316 and 15579611
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....ab07ad303617fd1d6d07fd8098ec023e