• editor@ijmra.in
  • ISSN[Online] : 2643-9875  ||  ISSN[Print] : 2643-9840

VOLUME 06 ISSUE 05 MAY 2023

A Taxonomy for Deep Learning in Dynamic Adaptive Video Streaming Over HTTP
Koffka Khan
Department of Computing and Information Technology, the University of the West Indies, St Augustine,Trinidad and Tobago, W.I.
DOI : https://doi.org/10.47191/ijmra/v6-i5-60

Google Scholar Download Pdf
ABSTRACT:

Deep Learning (DL) has become a fundamental technology in the field of Dynamic Adaptive Video Streaming over HTTP (DASH), enabling significant advancements in video streaming systems. This taxonomy presents a novel framework for categorizing and organizing the diverse applications and methodologies of DL in DASH. The taxonomy encompasses various aspects of DL, including video representation, quality of experience (QoE) estimation, bitrate adaptation, buffer management, content- and context-aware adaptation, and network optimization. By providing a comprehensive overview of DL in DASH, this taxonomy serves as a valuable resource for researchers and practitioners, facilitating a better understanding of the different DL techniques and their applications in enhancing video streaming performance and user experience.

KEYWORDS:

Deep Learning, DASH, QoE, network, taxonomy, streaming

REFERENCES

1) Ashok Kumar, P. M., Arun Raj, L. N., Jyothi, B., Soliman, N. F., Bajaj, M., & El-Shafai, W. (2022). A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support. Sensors, 22(23), 9307.

2) Baía Reis, A., & Ashmore, M. (2022). From video streaming to virtual reality worlds: an academic, reflective, and creative study on live theatre and performance in the metaverse. International Journal of Performance Arts and Digital Media, 18(1), 7-28.

3) Behravesh, R., Rao, A., Perez-Ramirez, D. F., Harutyunyan, D., Riggio, R., & Boman, M. (2022). Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming over HTTP (DASH). IEEE Transactions on Network and Service Management.

4) Behravesh, R., Rao, A., Perez-Ramirez, D. F., Harutyunyan, D., Riggio, R., & Boman, M. (2022). Machine Learning at the Mobile Edge: The Case of Dynamic Adaptive Streaming over HTTP (DASH). IEEE Transactions on Network and Service Management.

5) Biernacki, A. (2022). Improving Streaming Video with Deep Learning-Based Network Throughput Prediction. Applied Sciences, 12(20), 10274.

6) Cong, J., Zheng, P., Bian, Y., Chen, C. H., Li, J., & Li, X. (2022). A machine learning-based iterative design approach to automate user satisfaction degree prediction in smart product-service system. Computers & Industrial Engineering, 165, 107939.

7) Dao, N. N., Tran, A. T., Tu, N. H., Thanh, T. T., Bao, V. N. Q., & Cho, S. (2022). A contemporary survey on live video streaming from a computation-driven perspective. ACM Computing Surveys, 54(10s), 1-38.

8) de Sousa, N. F. S., Islam, M. T., Mustafa, R. U., Perez, D. A. L., Rothenberg, C. E., & Gomes, P. H. (2022). Machine learningassisted closed-control loops for beyond 5g multi-domain zero-touch networks. Journal of Network and Systems Management, 30(3), 46.

9) Hsu, C. F., Hung, T. H., & Hsu, C. H. (2022). Optimizing immersive video coding configurations using deep learning: A case study on TMIV. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18(1), 1-25.

10) Huang, T., Zhou, C., Zhang, R. X., Wu, C., & Sun, L. (2022). Learning tailored adaptive bitrate algorithms to heterogeneous network conditions: A domain-specific priors and meta-reinforcement learning approach. IEEE Journal on Selected Areas in Communications, 40(8), 2485-2503.

11) Khan, K., & Goodridge, W. (2018). QoE in DASH. International Journal of Advanced Networking and Applications, 9(4), 3515-3522.

12) Khan, K., & Goodridge, W. (2020). Reinforcement Learning in DASH. International Journal of Advanced Networking and Applications, 11(5), 4386-4392.

13) KHAN, K., & GOODRIDGE, W. (2022). Ultra-HD Video Streaming in 5G Fixed Wireless Access Bottlenecks.

14) Koffka, K., & Wayne, G. (2018). A DASH Survey: the ON-OFF Traffic Problem and Contemporary Solutions. Computer Sciences and Telecommunications, (1), 3-20.

15) Li, Z., Li, J., Wu, Q., Tyson, G., & Xie, G. (2022). A Large-Scale Measurement and Optimization of Mobile Live Streaming Services. IEEE Transactions on Mobile Computing.

16) Lim, W. M., Kumar, S., & Ali, F. (2022). Advancing knowledge through literature reviews:‘what’,‘why’, and ‘how to contribute’. The Service Industries Journal, 42(7-8), 481-513.

17) Lin, L., Di, L., Zhang, C., Guo, L., Di, Y., Li, H., & Yang, A. (2022). Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm. Scientific Data, 9(1), 63.

18) Liu, T., Xu, M., Li, S., Chen, C., Yang, L., & Lv, Z. (2023, June). Learnt Mutual Feature Compression for Machine Vision. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.

19) Liu, X. (2023). Quality of service for video stream over IP networks.

20) Loseto, G., Scioscia, F., Ruta, M., Gramegna, F., & Bilenchi, I. (2023). Semantic-based Adaptation of Quality of Experience in Web Multimedia Streams.

21) Motaung, W., Ogudo, K. A., & Chabalala, C. (2022, August). Real-time monitoring of video quality in a dash-based digital video broadcasting using deep learning. In 2022 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) (pp. 1-6). IEEE.

22) Murad, T., Nguyen, A., & Yan, Z. (2022, October). DAO: Dynamic Adaptive Offloading for Video Analytics. In Proceedings of the 30th ACM International Conference on Multimedia (pp. 3017-3025).

23) Naik, S., Khan, O., Katre, A., & Keskar, A. (2022, May). ARMPC-ARIMA based prediction model for Adaptive Bitrate Scheme in Streaming. In 2022 1st International Conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing (PCEMS) (pp. 107-112). IEEE.

24) Sanborn, K., Richardson, A., & Sprinkle, J. (2022, May). Semantic Tagging of CAN and Dash Camera Data from Naturalistic Drives. In 2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS) (pp. 312-313). IEEE.

25) Seng, J. K. P., Ang, K. L. M., Peter, E., & Mmonyi, A. (2022). Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing. Electronics, 11(14), 2239.

26) Sharaf Addin, E. H., Admodisastro, N., Mohd Ashri, S. N. S., Kamaruddin, A., & Chong, Y. C. (2022). Customer mobile behavioral segmentation and analysis in telecom using machine learning. Applied Artificial Intelligence, 36(1), 2009223.

27) Shishkov, B., Ivanova, K., Verbraeck, A., & van Sinderen, M. (2022, December). Combining context-awareness and data analytics in support of drone technology. In Telecommunications and Remote Sensing: 11th International Conference, ICTRS 2022, Sofia, Bulgaria, November 21–22, 2022, Proceedings (pp. 51-60). Cham: Springer Nature Switzerland.

28) Viola, R., Zorrilla, M., Angueira, P., & Montalbán, J. (2022). Multi-access Edge Computing video analytics of ITU-T P. 1203 Quality of Experience for streaming monitoring in dense client cells. Multimedia Tools and Applications, 81(9), 1238712403.

29) Wang, N. (2022). Application of DASH client optimization and artificial intelligence in the management and operation of big data tourism hotels. Alexandria Engineering Journal, 61(1), 81-90.

30) Wei, B., Song, H., Nguyen, Q. N., & Katto, J. (2022, January). DASH Live Video Streaming Control Using Actor-Critic Reinforcement Learning Method. In Mobile Networks and Management: 11th EAI International Conference, MONAMI 2021, Virtual Event, October 27-29, 2021, Proceedings (pp. 17-24). Cham: Springer International Publishing.

31) Zhao, Jia, Jiangchuan Liu, Haiyang Wang, Changqiao Xu, and Hongke Zhang. "Multipath Congestion Control: Measurement, Analysis, and Optimization From the Energy Perspective." IEEE Transactions on Network Science and Engineering (2023).

VOLUME 06 ISSUE 05 MAY 2023

There is an Open Access article, distributed under the term of the Creative Commons Attribution – Non Commercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting and building upon the work for non-commercial use, provided the original work is properly cited.


Our Services and Policies

Authors should prepare their manuscripts according to the instructions given in the authors' guidelines. Manuscripts which do not conform to the format and style of the Journal may be returned to the authors for revision or rejected.

The Journal reserves the right to make any further formal changes and language corrections necessary in a manuscript accepted for publication so that it conforms to the formatting requirements of the Journal.

International Journal of Multidisciplinary Research and Analysis will publish 12 monthly online issues per year,IJMRA publishes articles as soon as the final copy-edited version is approved. IJMRA publishes articles and review papers of all subjects area.

Open access is a mechanism by which research outputs are distributed online, Hybrid open access journals, contain a mixture of open access articles and closed access articles.

International Journal of Multidisciplinary Research and Analysis initiate a call for research paper for Volume 07 Issue 05 (May 2024).

PUBLICATION DATES:
1) Last Date of Submission : 26 May 2024 .
2) Article published within a week.
3) Submit Article : editor@ijmra.in or Online

Why with us

International Journal of Multidisciplinary Research and Analysis is better then other journals because:-
1 : IJMRA only accepts original and high quality research and technical papers.
2 : Paper will publish immediately in current issue after registration.
3 : Authors can download their full papers at any time with digital certificate.

The Editors reserve the right to reject papers without sending them out for review.

Authors should prepare their manuscripts according to the instructions given in the authors' guidelines. Manuscripts which do not conform to the format and style of the Journal may be returned to the authors for revision or rejected. The Journal reserves the right to make any further formal changes and language corrections necessary in a manuscript accepted for publication so that it conforms to the formatting requirements of the Journal.

Indexed In
Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar Avatar