| dc.creator |
Mongi, Alex Frank |
|
| dc.date |
2020-09-30T08:10:58Z |
|
| dc.date |
2020-09-30T08:10:58Z |
|
| dc.date |
2017 |
|
| dc.date.accessioned |
2022-10-20T13:46:22Z |
|
| dc.date.available |
2022-10-20T13:46:22Z |
|
| dc.identifier |
Mongi, A. F. (2017). Developing video streaming quality of experience prediction model in wireless broadband networks (Doctoral thesis). The University of Dodoma, Dodoma. |
|
| dc.identifier |
http://hdl.handle.net/20.500.12661/2488 |
|
| dc.identifier.uri |
http://hdl.handle.net/20.500.12661/2488 |
|
| dc.description |
Doctoral thesis |
|
| dc.description |
Mobile video streaming has grown tremendously in recent years due to technological advancement of both network and end-devices. Unlike the voice services which run on the background traffic mode, the network impairments on video streaming traffic affect users’ perceived quality directly which results into high rate of churn. For that reason, measuring users’ quality of experience (QoE) has become the major challenge to network operators and service providers. This study therefore, aimed at developing a mathematical model for predicting video streaming QoE in wireless broadband networks. The systematic literature review and survey methods were used to identify the variables affecting user QoE through frequency counts and correlation analysis. Furthermore, experiments were conducted using emulation technique over a wireless broadband network test-bed to investigate the effects of identified variables on video streaming QoE. The data extracted were analysed by using the Taguchi method, 3-way Anova and 5-way Anova.
This study found that, pixel density index and viewing distance of smart devices’ screen had significant effect on video streaming QoE. Moreover, bit rate, frame rate and content type significantly affected video streaming QoE. The network impairments due to delay and jitter were also found to be more destructive on video streaming QoE compared to packet loss. Eventually, the nonlinear mathematical model was developed by basing on the combined effects of content type, bit rate, delay, jitter and pixel density index. It achieved high level of prediction accuracy in three content types grouped into slow, medium and fast moving contents. |
|
| dc.language |
en |
|
| dc.publisher |
The University of Dodoma |
|
| dc.subject |
Mobile video streaming |
|
| dc.subject |
Technological advancement |
|
| dc.subject |
Network |
|
| dc.subject |
Video streaming |
|
| dc.subject |
Wireline broadband networks |
|
| dc.subject |
Wireless broadband networks |
|
| dc.subject |
Broadband Networks |
|
| dc.subject |
Mobile video |
|
| dc.subject |
Broadband networks |
|
| dc.subject |
Mobile technology |
|
| dc.subject |
Mobile traffic |
|
| dc.subject |
Network emulator |
|
| dc.subject |
Wireless broadband |
|
| dc.title |
Developing video streaming quality of experience prediction model in wireless broadband networks |
|
| dc.type |
Thesis |
|