HTBT: A Hybrid DASH Adaptation Algorithm Using Takagi-Sugeno-Kang Fuzzy Model
Video streaming takes the largest share of internet traffic today, and MPEG dynamic adaptive streaming over HTTP (DASH) has become dominant among other video streaming standards and protocols. According to the DASH standard, multimedia content is encoded in different quality levels with different...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2023-02-01
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| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2023.01001 |
| Summary: | Video streaming takes the largest share of internet traffic today, and MPEG dynamic adaptive streaming
over HTTP (DASH) has become dominant among other video streaming standards and protocols. According
to the DASH standard, multimedia content is encoded in different quality levels with different bitrates
located on the server, and users can request multimedia content of any available bitrate. The user
side determines the desired bitrate in the unit called adaptation bitrate (ABR) logic. Many ABR
algorithms have been proposed to improve the quality of experience (QoE). The main criteria for
determining QoE are average bitrate, number of switches between resolutions, and number of buffer
underflows. This paper presents a hybrid DASH adaptation algorithm that uses the following input
values: current buffer occupancy level, network throughput value calculated on the last downloaded
DASH segment, and Takagi-Sugeno-Kang model output that represents expected throughput in the next
segment download iteration. We compared the proposed algorithm with several other algorithms and
the results show that it outperforms others in average bitrate and number of bitrate switches.
Furthermore, our algorithm prevented all buffer underflows. |
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| ISSN: | 1582-7445 1844-7600 |