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-i5-60Google 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
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