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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Claeys, M. Latré, S. Famaey, J. De Turck, F. |
| Copyright Year | 1997 |
| Abstract | HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the end-user. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment. |
| Starting Page | 716 |
| Ending Page | 719 |
| Page Count | 4 |
| File Size | 253182 |
| File Format | |
| ISSN | 10897798 |
| Volume Number | 18 |
| Issue Number | 4 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Streaming media Video sequences Bandwidth Standards Bit rate Adaptive systems Convergence quality of service learning systems intelligent agent |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering Modeling and Simulation Computer Science Applications |
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