Improved DASH Architecture for Quality Cloud Video Streaming in Automated Systems

Main Article Content

S. Vijayalakshmi
S. Vishnupriya
B. Sarala
Bhuvan Karthik Ch.
R. Dhanalakshmi
J. Jasmine Hephzipah
R. Pavaiyarkarasi

Abstract

In modern times, multimedia streaming systems that transmit video across a channel primarily use HTTP services as a delivery component. Encoding the video for all quality levels is avoided thanks to fuzzy based encoders' ability to react to network changes. Additionally, the system frequently uses packet priority assignment utilising a linear error model to enhance the dynamic nature of DASH without buffering. Based on a fuzzy encoder, the decision of video quality is made in consideration of the bandwidth available. This is a component of the MPEG DASH encoder. The Fuzzy DASH system seeks to increase the scalability of online video streaming, making it suitable for live video broadcasts through mobile and other devices.

Article Details

How to Cite
Vijayalakshmi, S. ., Vishnupriya, S. ., Sarala, B. ., Karthik Ch., B. ., Dhanalakshmi, R. ., Hephzipah, J. J. ., & Pavaiyarkarasi, R. . (2023). Improved DASH Architecture for Quality Cloud Video Streaming in Automated Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 32–42. https://doi.org/10.17762/ijritcc.v11i2s.6026
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References

Adams, J. and Muntean, G.M., 2007, May. Power save adaptation algorithm for multimedia streaming to mobile devices. In Portable Information Devices, 2007. PORTABLE07. IEEE International Conference on (pp. 1-5). IEEE.

Adzic, V., Kalva, H. and Furht, B., 2012. Optimizing video encoding for adaptive streaming over HTTP. IEEE Transactions on Consumer Electronics, 58(2).

Akhshabi, S., Narayanaswamy, S., Begen, A.C. and Dovrolis, C., 2012. An experimental evaluation of rate-adaptive video players over HTTP. Signal Processing: Image Communication, 27(4), pp.271-287.

Andelin, T., Chetty, V., Harbaugh, D., Warnick, S. and Zappala, D., 2012, February. Quality selection for dynamic adaptive streaming over HTTP with scalable video coding. In Proceedings of the 3rd Multimedia Systems Conference (pp. 149-154). ACM.

Ayad, I., Im, Y., Keller, E. and Ha, S., 2018. A Practical Evaluation of Rate Adaptation Algorithms in HTTP-based Adaptive Streaming. Computer Networks, 133, pp.90-103.

Basso, S., Servetti, A., Masala, E. and De Martin, J.C., 2014, March. Measuring DASH streaming performance from the end users perspective using neubot. In Proceedings of the 5th ACM multimedia systems conference (pp. 1-6). ACM.

Beben, A., Wi?niewski, P., Batalla, J.M. and Krawiec, P., 2016, May. ABMA+: lightweight and efficient algorithm for HTTP adaptive streaming. In Proceedings of the 7th International Conference on Multimedia Systems (p. 2). ACM.

Bentaleb, A., Begen, A.C. and Zimmermann, R., 2018. QoE-Aware Bandwidth Broker for HTTP Adaptive Streaming Flows in an SDN-Enabled HFC Network. IEEE Transactions on Broadcasting, 64(2), pp.575-589.

Birkedal, E., Griwodz, C. and Halvorsen, P., 2007, December. Implementation and evaluation of Late Data Choice for TCP in Linux. In Multimedia, 2007. ISM 2007. Ninth IEEE International Symposium on (pp. 221-228). IEEE.

Bokani, A., Hassan, M., Kanhere, S. and Zhu, X., 2015. Optimizing HTTP-based adaptive streaming in vehicular environment using markov decision process. IEEE Transactions on Multimedia, 17(12), pp.2297-2309.

Bouten, N., Schmidt, R.D.O., Famaey, J., Latré, S., Pras, A. and De Turck, F., 2015. QoE-driven in-network optimization for Adaptive Video Streaming based on packet sampling measurements. Computer networks, 81, pp.96-115.

J.JasmineHephzipah,P.Thirumurugan”Performance Analysis of Meningioma Brain Tumor Detection System Using Feature Learning Optimization and ANFIS Classification Method”IETE Journal of Research,volume no.68,Issue No.2,

Jasmine Hephzipah Johnpeter, Thirumurugan Ponnuchamy Computer aided automated detection and classification of brain tumors using CANFIS classification method, 28 March2019, 29(4)

Classical Energy Detection Method For Spectrum Detecting in Cognitive Radio Networks by using robust augmented threshold techniques, B.Sarala, D.Rukmani Devi, D.S. Bargava, cluster computing,Springer - the journal of networks software Tools and applications, Springer, 22,11109-11118 ( Sep 2019) Impact factor -3.458, science citation index, expanded [scvisearh], Scopus, Google scholar, WOS, pp. 1-10. https://doi.org/ 10.1007/s10586-017-1311-8, volume 22, issue -5, 1.9. 2019., page 11109-11118, cited by 7, ISSN no : 1386-7857

Spectrum energy detection in cognitive radio networks based on a novel adaptive threshold energy detection method, B.Sarala , S.Rukmani Devi &J.JoselinJeya Sheela computer communications, Elsevier, volume 152, 9 Jan 2020, page 1-7, Science direct, Scopus ,SCI ISSN- 0140 – 3664 https://doi.org/10.1016/j.comcom.2019.12.058, Impact Factor: 4.08, cited by 28.

Simulation and comparison of single and differential ended CG-CS LNA for CognitiveRadio, B.Sarala, S.Rukmani Devi, Jasmine Hepzibah, P.Gunasekhar , J. Joseline Jeya Sheela- International journal of wavelets multiresolution and Information Processing, April 2021, Page no: 2150013, SCIE,Scopus.https://doi.org/10.1142/S0219691321500132, Vol. 19, No. 05, 2150013 (2021),impact factor: 1.04, cited by 1.

Lab view based non-invasive single channel field electrocardiogram extraction IEEE, Suganthy M, Immaculate Joy S, B.Sarala, International conference on energy system & Information Processing (ICESIP), June 2019, cited by 4

Vehicle Seat Vacancy Identification Using Image Processing Technique, Darwin Nesakumar A, Suresh T , Kanimozhi P, Lokeshwari A , Manjuparkavi T , B.sarala, P.Mugila - AIP Publishing,2519,050023(2022) – Scopus,WoS, https://doi.org/10.1063/5.0109641

Automated Seed Sowing and Watering Robot using Wireless Sensor Network,Dr.M.Somasundaram , A.Naveen Kumar, B.Nikhil Vamsi, B.VishalChowdary,S.P.Karthikeyan, B.Sarala-AIP Publishing – Scopus, WOS,2519,050027(2022), https://doi.org/10.1063/ 5.0109648

R.Sujatha, MahaboobBasha.S, B.Sarala, J.JasmineHepzhipah, N.G..Praveena, IoT Enabled Smart Logistics Vehicle using Semantic Communication, International journal of Intelligent Systems & Applications in Engineering,vol10,issue4https://ijisae.org/index.php/IJISAE/article/view/2317, (accessed on 24 December 2022)

Krasic, C., Walpole, J. and Feng, W.C., 2003, June. Quality-adaptive media streaming by priority drop. In Proceedings of the 13th international workshop on Network and operating systems support for digital audio and video (pp. 112-121). ACM.

Krasic, C., Walpole, J. and Feng, W.C., 2003, June. Quality-adaptive media streaming by priority drop. In Proceedings of the 13th international workshop on Network and operating systems support for digital audio and video (pp. 112-121). ACM.

Kreuzberger, C., Posch, D. and Hellwagner, H., 2015, March. A scalable video coding dataset and toolchain for dynamic adaptive streaming over HTTP. In Proceedings of the 6th ACM Multimedia Systems Conference (pp. 213-218). ACM.

Krishnamoorthi, V., Bergström, P., Carlsson, N., Eager, D., Mahanti, A. and Shahmehri, N., 2013, August. Empowering the creative user: personalized HTTP-based adaptive streaming of multi-path nonlinear video. In ACM SIGCOMM Computer Communication Review (Vol. 43, No. 4, pp. 53-58). ACM.

Krishnamoorthi, V., Carlsson, N., Eager, D., Mahanti, A. and Shahmehri, N., 2014, November. Quality-adaptive prefetching for interactive branched video using http-based adaptive streaming. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 317-326). ACM.

Krishnamoorthi, V., Carlsson, N., Halepovic, E. and Petajan, E., 2017, June. BUFFEST: Predicting Buffer Conditions and Real-time Requirements of HTTP (S) Adaptive Streaming Clients. In Proceedings of the 8th ACM on Multimedia Systems Conference (pp. 76-87). ACM.

Kumar, S., Sarkar, A. and Sur, A., 2017. A resource allocation framework for adaptive video streaming over LTE. Journal of Network and Computer Applications, 97, pp.126-139.

Kuschnig, R., Kofler, I. and Hellwagner, H., 2011, February. Evaluation of HTTP-based request-response streams for internet video streaming. In Proceedings of the second annual ACM conference on Multimedia systems (pp. 245-256). ACM.

Layaida, O. and Hagimont, D., 2005, June. Designing self-adaptive multimedia applications through hierarchical reconfiguration. In IFIP International Conference on Distributed Applications and Interoperable Systems (pp. 95-107). Springer, Berlin, Heidelberg.

Lee, J., 2005. Scalable continuous media streaming systems: Architecture, design, analysis and implementation. John Wiley & Sons.

Lee, Y.C., Kim, J., Altunbasak, Y. and Mersereau, R.M., 2003, May. Performance comparisons of layered and multiple description coded video streaming over error-prone networks. In Communications, 2003. ICC'03. IEEE International Conference on (Vol. 1, pp. 35-39). IEEE.

Li, Z., Begen, A.C., Gahm, J., Shan, Y., Osler, B. and Oran, D., 2014, March. Streaming video over HTTP with consistent quality. In Proceedings of the 5th ACM multimedia systems conference (pp. 248-258). ACM.

Liang, K., Hao, J., Zimmermann, R. and Yau, D.K., 2015, March. Integrated prefetching and caching for adaptive video streaming over HTTP: an online approach. In Proceedings of the 6th ACM Multimedia Systems Conference (pp. 142-152). ACM.

Lin, Y.T., Bonald, T. and Elayoubi, S.E., 2018. Flow-level traffic model for adaptive streaming services in mobile networks. Computer Networks, 137, pp.1-16.

Liotou, E., Samdanis, K., Pateromichelakis, E., Passas, N. and Merakos, L., 2018. QoE-SDN APP: A Rate-guided QoE-aware SDN-APP for HTTP Adaptive Video Streaming. IEEE Journal on Selected Areas in Communications.

Liu, C., Bouazizi, I. and Gabbouj, M., 2011, February. Rate adaptation for adaptive HTTP streaming. In Proceedings of the second annual ACM conference on Multimedia systems (pp. 169-174). ACM.

Liu, C., Bouazizi, I., Hannuksela, M.M. and Gabbouj, M., 2012. Rate adaptation for dynamic adaptive streaming over HTTP in content distribution network. Signal Processing: Image Communication, 27(4), pp.288-311.

Liu, E. and Temlyakov, V.N., 2012. The orthogonal super greedy algorithm and applications in compressed sensing. IEEE Transactions on Information Theory, 58(4), pp.2040-2047.

Liu, J., Xie, R. and Yu, F.R., 2016. Resource allocation and user association for HTTP adaptive streaming in heterogeneous cellular networks with small cells. China Communications, 13(9), pp.1-11.

Ma, K.J., Bartos, R., Bhatia, S. and Nair, R., 2011. Mobile video delivery with HTTP. IEEE Communications Magazine, 49(4).

Mahapatra, S., 2018. Quality of Experience Driven Rate Adaptation for Adaptive HTTP Streaming. IEEE Transactions on Broadcasting, DOI: 10.1109/TBC.2018.2799301

Meng, S., Sun, J., Duan, Y. and Guo, Z., 2016. Adaptive Video Streaming With Optimized Bitstream Extraction and PID-Based Quality Control. IEEE Transactions on Multimedia, 18(6), pp.1124-1137.

Michalos, M.G., Kessanidis, S.P. and Nalmpantis, S.L., 2012. Dynamic adaptive streaming over HTTP. Journal of Engineering Science and Technology Review, 5(2), pp.30-34.

Crptography based LiFi for patient privacy & Emergency health Service Using ioT, Vithya V.T,M.O. Chandrasekar , M.Suganthy, J.Jasmine hephzipah,B.sarala, N.G.Praveena, M.Perarasi – International journal on Recent & Innovation Trends in Computing & Communications,vol 10 No 2S (2022) – Scopus, 10.17762/ijritcc.v10i2s.592, https://ijritcc.org/index.php/ijritcc/article/view/5928