ZTE Communications ›› 2018, Vol. 16 ›› Issue (3): 23-29.DOI: 10.19729/j.cnki.1673-5188.2018.03.005
-
出版日期:2018-08-25发布日期:2020-03-18
How to Manage Multimedia Traffic: Based on QoE or QoT?
Amulya Karaadi, Is-Haka Mkwawa, Lingfen Sun
- School of Computing, Electronics and Mathematics, University of Plymouth, Plymouth, PL4 8AA, United Kingdom
-
Online:2018-08-25Published:2020-03-18 -
About author:Amulya Karaadi (Amulya.karaadi@plymouth.ac.uk) received her master’s degree in computer systems networking and telecommunications from Staffordshire University, UK in 2016 and bachelor’s degree in information technology from JNTU University, India in 2013. She is now pursuing her Ph.D. at the University of Plymouth, UK in the area of intelligent multimedia transmissions for the Internet of Things (IoT) applications.|Is-Haka Mkwawa (Is-Haka.Mkwawa@plymouth.ac.uk) received his Ph.D. in computing from the University of Bradford, UK in 2004. He has been working in various capacities on EU FP6, FP7 and Horizon 2020 projects since 2002 with the University of Bradford, the University College Dublin and the University of Plymouth. These projects included IASON (2002-2004), Euro-NGI (2003-2005), Euro-FGI (2005-2008), Science Foundation Ireland (2005-2006), FP6 Vital (2006-2008), FP7 ADAMANTIUM (2008-2010) and FP7 GERYON (2011-2014). He has authored several refereed publication and co-authored Guide to Voice and Video over IP: For Fixed and Mobile Networks (Springer, 2013). His research interests include IMS media plane security for next generation of emergency communication and services, QoE control and management, mobility management in mobile and wireless networks, software defined networking, power saving in IoT, overlay networks, performance analysis and evaluation of IMS mobility management, parallel computing, and collective communication.|Lingfen Sun (L.Sun@plymouth.ac.uk) received the B.Eng. degree in telecommunication engineering and the M.Sc. degree in communications and electronic system from the Institute of Communication Engineering, China and the Ph.D. degree in computing and communications from the University of Plymouth, UK. She is currently an associate professor (Reader) in multimedia communications and networks in the School of Computing, Electronics and Mathematics, University of Plymouth. She has been involved in several European projects including H2020 QoE-NET as PI, COST Action QUALINET as an MC member, FP7 GERYON as PI and Scientific Manager and FP7 ADAMANTIUM as Co-PI and WP leader. She has published one book and over 90 peer-refereed technical papers/book chapters since 2000. She was the Chair of QoE Interest Group of IEEE MMTC during 2010-2012, and Symposium Co-Chair for IEEE ICC’14. She has been an AE for IEEE Transactions on Multimedia (2016-2018) and an expert reviewer for grants for EU, EPSRC (UK) and NSERC (Canada). Her main research interests include multimedia networking, multimedia quality assessment, QoS/QoE management, VoIP, DASH and SDN/NFV.
引用本文
. [J]. ZTE Communications, 2018, 16(3): 23-29.
Amulya Karaadi, Is-Haka Mkwawa, Lingfen Sun. How to Manage Multimedia Traffic: Based on QoE or QoT?[J]. ZTE Communications, 2018, 16(3): 23-29.
Figure 1. Conceptual diagram for internet multimedia services.
Figure 1. Conceptual diagram for internet multimedia services.
Figure 2. Things, edge and cloud layers.
Figure 2. Things, edge and cloud layers.
Figure 3. An IoT scenario for surveillance.
Figure 3. An IoT scenario for surveillance.
Table 1 Human detection video sequence
| Video sequence | Resolution (pixels) | Bitrate (kbit/s) | Frame-rate (fps) |
|---|---|---|---|
| Human detection | 768×576 | 5-800 | 25 |
Table 1 Human detection video sequence
| Video sequence | Resolution (pixels) | Bitrate (kbit/s) | Frame-rate (fps) |
|---|---|---|---|
| Human detection | 768×576 | 5-800 | 25 |
Figure 4. Sample video sequences.
Figure 4. Sample video sequences.
Table 2 License number plate image
| Video sequence | Resolution (pixels) | Quality range (%) |
|---|---|---|
| Car1 | 640×480 | 1-90 |
Table 2 License number plate image
| Video sequence | Resolution (pixels) | Quality range (%) |
|---|---|---|
| Car1 | 640×480 | 1-90 |
Figure 5. Human detection video frames.
Figure 5. Human detection video frames.
Figure 6. Human detection accuracy for frames 1, 44 and 102.
Figure 6. Human detection accuracy for frames 1, 44 and 102.
Figure 7. Quality of frames 1, 44 and 102.
Figure 7. Quality of frames 1, 44 and 102.
Figure 8. Human detection video frames at 50 kbit/s.
Figure 8. Human detection video frames at 50 kbit/s.
Figure 9. Car1 number plate recognition accuracy.
Figure 9. Car1 number plate recognition accuracy.
Figure 10. SSIM values of the Car1 image.
Figure 10. SSIM values of the Car1 image.
Figure 11. Car1 number plate sequence frame size.
Figure 11. Car1 number plate sequence frame size.
Figure 12. Car1 number plate at quality 5%.
Figure 12. Car1 number plate at quality 5%.
| [1] | Internet live stats. ( 2017). Internet live stats [online]. Available: |
| [2] | Statista. ( 2017). Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025(in billions) [online]. Available: |
| [3] | Cisco. (2017). Visual Networking Index: Forecast and Method-ology, 2016-2021 [online] . Available: |
| [4] | A. Alfayly, I. Mkwawa, L. Sun, E. Ifeachor , “QoE-driven LTE downlink scheduling for VoIP application,” in IEEE Consumer Communications and Networking Conference (CCNC), Las Vegas, USA, Jan. 2015, pp. 603-604. doi: 10.1109/CCNC.2015.7158043. |
| [5] | A. Karaadi, L. Sun, I. Mkwawa , “Multimedia communications in internet of things QoT or QoE?,” in IEEE International Conference on Cyber, Physical and Social Computing (CPSCOM), Exeter, UK, Jun. 2017, pp. 23-29. doi: 10.1109/iThings-GreenCom-CPSCom-SmartData.2017.11. |
| [6] | C. Long, Y. Cao, T. Jiang, Q. Zhang , “Edge computing framework for cooperative video processing in multimedia IoT systems,” IEEE Transactions on Multimedia, vol. 20, no.5,pp. 1126-1139, Oct. 2017. doi: 10.1109/TMM.2017. 2764330. |
| [7] | T. Soyata, R. Muraleedharan, C. Funai, M. Kwon, W. Heinzelman , “Cloud-vision: real-time face recognition using a mobile-cloudlet-cloud acceleration architecture,” in IEEE Symposium on Computers and Communications (ISCC), Cappadocia, Turkey, Jul. 2012, pp. 59-66. doi: 10.1109/ISCC.2012.6249269. |
| [8] | A. H. M .Amin, N. M. Ahmad, and A. M. M. Ali, “Decentralized face recognition scheme for distributed video surveillance in IoT-cloud infrastructure,” in IEEE Region 10 Symposium (TENSYMP), Bali, Indonesia, May 2016, pp. 119-124. doi: 10.1109/TENCONSpring.2016.7519389. |
| [9] | W. Wang, Q. Wang, K. Sohraby , “Multimedia sensing as a service (MSaaS): exploring resource saving potentials of at cloud-edge IoTs and Fogs,” IEEE Internet of Things Journal, vol. 4, no.2,pp. 487-495, Jun. 2016. doi: 10.1109/JIOT.2016.2578722. |
| [10] | N. Chen, Y. Chen, Y. You , et al., “Dynamic urban surveillance video stream processing using fog computing,” in IEEE Second International Conference on Multimedia Big Data, Apr. 2016, pp. 105-112. doi: 10.1109/BigMM.2016.53. |
| [11] | X. Chen, J. N. Hwang, D. Meng , et al., “A quality-of-content-based joint source and channel coding for human detections in a mobile surveillance cloud,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 27, no.1,pp. 19-31, Mar. 2016. doi: 10.1109/TCSVT.2016.2539758. |
| [12] | L. Zhao, X. Zhang, X. Zhang , et al., “Intelligent analysis oriented surveillance video coding,” in IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China, Jul. 2017, pp. 37-42. doi: 10.1109/ICME.2017.8019429. |
| [13] | D. G. Costa, M. Collotta, G. Pau, C. D. Faundez , “A fuzzy-based approach for sensing, coding and transmission configuration of visual sensors in smart city applications,” Sensors, vol. 17, no. 1, 2017. doi: 10.3390/s17010093. |
| [14] | A. Floris and L. Atzori, “Quality of experience in the multimedia internet of things: definition and practical use cases,” in IEEE International Conference on Communication Workshop (ICCW), London, UK, Jun. 2015, pp. 1747-1752. doi: 10.1109/ICCW.2015.7247433. |
| [15] | OpenCV. ( 2017). OpenCV [online]. Available: |
| [16] | N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, Jun. 2005, pp. 886-893. doi: 10.1109/CVPR. 2005.177. |
| [17] | OpenALPR. (2017). OpenALPR [online]. Available: |
| [18] | P. F. Felzenszwalb, R. B. Girshick, D. McAllester, D. Ramanan , “Object detection with discriminatively trained part-based models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no.9,pp. 1627-1645, Sept. 2009. doi: 10.1109/TPAMI.2009.167. |
| [19] | Z. Wang, L. Lu, A. C. Bovik , “Video quality assessment based on structural distortion measurement,” Signal Processing: Image Communication, vol. 19, no. 2, pp. 121-132, 2004. doi: 10.1016/S0923-5965(03)00076-6. |
| [20] | A. N. Moldovan, I. Ghergulescu, C. H. Muntean , “VQAMap: a novel mechanism for mapping objective video quality metrics to subjective MOS scale,” IEEE Transactions on Broadcasting, vol. 62, no.3,pp. 610-627, Jun. 2016. doi: 10.1109/TBC.2016.2570002. |
| No related articles found! |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||