BANQUET: Balancing Quality of Experience and Traffic Volume in Adaptive Video Streaming
| Autor: | Jun Okamoto, Matsumoto Arifumi, Kimura Takuto, Tatsuaki Kimura |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Mean opinion score Control (management) Real-time computing ComputerApplications_COMPUTERSINOTHERSYSTEMS 020206 networking & telecommunications Throughput 02 engineering and technology Set (abstract data type) Limit (music) 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Quality of experience TRACE (psycholinguistics) |
| Zdroj: | CNSM |
| DOI: | 10.23919/cnsm46954.2019.9012685 |
| Popis: | Bitrate-selection algorithms are key to improving the quality of experience (QoE) of adaptive video streaming. Although current bitrate selection algorithms maximize the QoE, video consumers are concerned with QoE and traffic-volume usage due to the pay-per-use or data-capped plans. To balance between the QoE and traffic volume, some commercial video-streaming services enable users to set the upper limit of the selectable bitrate. However, it is difficult for users to set an appropriate limit to obtain sufficient QoE. We propose BANQUET, a novel bitrate-selection algorithm that enables users to control intuitively the balance between the QoE and traffic volume. Assuming a user-set target QoE as a balancing parameter, BANQUET selects the bitrate that minimizes the traffic volume while maintaining the estimated mean opinion score (MOS) above the target QoE. BANQUET calculates the appropriate bitrate based on estimations of the throughput and butter transition. A trace-based simulation shows that BANQUET reduces the traffic volume by up to 47.0% compared to a baseline while maintaining the same average estimated MOS. |
| Databáze: | OpenAIRE |
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