Top

Wireless Personal Communications

Published in:

15-02-2018

Framework for Fast and Efficient Cloud Video Transcoding System Using Intelligent Splitter and Hadoop MapReduce

Authors: D. Kesavaraja, A. Shenbagavalli

Published in: Wireless Personal Communications | Issue 3/2018

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The way computing was done has changed a lot in recent times. Nowadays mobile devices have been the stealing the show. These devices come in different specifications, that any multimedia content that is to be played requires transcoding for better user experiences. Cloud based video services cater to the needs of the end user based on their requirements, through video transcoding. Hence video transcoding plays a very important role in today’s evolving streaming media environment. The major problem with video transcoding is that it consumes a lot of time and impacts seriously on the quality of the output. Transcoding uses the device information to transform the video into the required format and this process is done in a distributed fashion, to speed up the process. This work proposes an Intelligent Video Splitter which uses the Map Reduce algorithm to provide efficiency based on time factor. The important performance metrics including video distortion (VD), video distortion due to frame dependency (FDD) were considered. The results showed that the proposed framework perceptibly outperforms than the prevailing strategies. It provides higher video quality as a result of it introduces less video distortion. In future this method may be extended to supply associate automatic device aware video standards.

previous article Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering

next article Cluster-PSO Based Resource Orchestration for Multi-task Applications in Vehicular Cloud

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Myoung, J. K., Yun, C. U. I., Lee, H. K., & Han, S. H. (2016). Method for transcoding multimedia, and cloud multimedia transcoding system operating the same. US Patent - US20160164941 A1.

Hwang, J., Lee, J., Choi, N., & Yoo, C. (2014). HAVS: Hybrid adaptive video streaming for mobile devices. IEEE Transactions on Consumer Electronics, 60(2), 210–216. https://doi.org/10.1109/tce.2014.6851996.CrossRef

Barekatain, B., Khezrimotlagh, D., Aizaini Maarof, M., Ghaeini, H., Salleh, S., Quintana, A., et al. (2013). MATIN: A Random network coding based framework for high quality peer-to-peer live video streaming. PLoS ONE, 8(8), e69844. https://doi.org/10.1371/journal.pone.0069844.CrossRef

Watkinson, J. (2010). The MPEG handbook. Amsterdam: Elsevier.

Hu, W., Xie, N., Li, L., Zeng, X., & Maybank, S. (2011). A survey on visual content-based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews. https://doi.org/10.1109/tsmcc.2011.2109710.

Barekatain, B., Khezrimotlagh, D., Maarof, M., Ghaeini, H., Quintana, A., & Cabrera, A. (2014). Efficient P2P live video streaming over hybrid WMNs using random network coding. Wireless Personal Communications, 80(4), 1761–1789. https://doi.org/10.1007/s11277-014-2112-5.CrossRef

Ghaeini, H., Akbari, B., Barekatain, B., & Trivino-Cabrera, A. (2015). Adaptive video protection in large scale peer-to-peer video streaming over mobile wireless mesh networks. International Journal of Communication Systems, 29(18), 2580–2603. https://doi.org/10.1002/dac.3088.CrossRef

Kesavaraja, D., & Shenbagavalli, A. (2017). Scalable robust implementation of reliable streaming (SRIRS) with tristate aware quality of Service. Wireless Personal Communications, 96(2), 2797–2820. https://doi.org/10.1007/s11277-017-4325-x.CrossRef

Ma, Z., Xie, J., Li, H., Sun, Q., Si, Z., Zhang, J., et al. (2017). The role of data analysis in the development of intelligent energy networks. IEEE Network, 31(5), 88–95. https://doi.org/10.1109/mnet.2017.1600319.CrossRef

10.

Vetro, A., Christopoulos, C., & Sun, Huifang. (2003). Video transcoding architectures and techniques: An overview. IEEE Signal Processing Magazine, 20(2), 18–29. https://doi.org/10.1109/msp.2003.1184336.CrossRef

11.

Keerthika Janani, M., & Sudhakar, G. (2014). HAVS: Hadoop based adaptive video streaming by the integration of cloudlets and stratus. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 3(2), 408–413.

12.

Dean, J., & Ghemawat, S. (2008). MapReduce. Communications of the ACM, 51(1), 107. https://doi.org/10.1145/1327452.1327492.CrossRef

13.

Malhotra, J., & Ghyare, P. (2014). A novel way of deduplication approach for cloud backup services using block index caching technique. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 3(7), 10888–10892. https://doi.org/10.15662/ijareeie.2014.0307040.CrossRef

14.

Khoshkholghi, M., Abdullah, A., Latip, R., Subramaniam, S., & Othman, M. (2014). Disaster recovery in cloud computing: A survey. Computer and Information Science, 7(4), 39. https://doi.org/10.5539/cis.v7n4p39.CrossRef

15.

Chahal, N. S., & Khehra, B. S. (2012). A comparative study for optimization of video file compression in cloud environment. International Journal of Computer Applications, 60(13), 27–30. https://doi.org/10.5120/9753-4334.CrossRef

16.

Pereira, R., Azambuja, M., Breitman, K., & Endler, M. (2010). An architecture for distributed high performance video processing in the cloud. In 2010 IEEE 3rd international conference on cloud computing. https://doi.org/10.1109/cloud.2010.73.

17.

Shanthi, B., & Prakash Narayanan, C. (2014). Dynamic resource allocation and distributed video transcoding using hadoop cloud computing. International Journal of Innovative Research in Computer and Communication Engineering, 2(1), 4075–4081.

18.

Wood, T., Cecchet, E., Ramakrishnan, K., Shenoy, P., van der Merwe J., & Venkataramani, A. (2010). Disaster recovery as a cloud service: Economic benefits and deployment challenges. In HotCloud’10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing (pp. 8–8). USENIX Association Berkeley, CA, USA.

19.

Ghemawat, S., Gobioff, H., & Leung, S. (2003). The Google file system. ACM SIGOPS Operating Systems Review, 37(5), 29. https://doi.org/10.1145/1165389.945450.CrossRef

20.

White, T. (2015). Hadoop: The definitive guide; storage and analysis at internet scale. Beijing: O’Reilly Media.

21.

Disco MapReduce. (2018). Discoproject.org. Retrieved January 19, 2018, from http://discoproject.org.

22.

Venkatesa Kumar, V., & Palaniswami, S. (2012). A dynamic resource allocation method for parallel dataprocessing in cloud computing. Journal of Computer Science, 8(5), 780–788. https://doi.org/10.3844/jcssp.2012.780.788.CrossRef

23.

Handle Proxy. (2018). Handle.itu.int. Retrieved January 19, 2018, from http://handle.itu.int.

24.

Liu, S, Quan, G., & Ren, S (2010). On-Line scheduling of real-time services for cloud computing. In 2010 6th World Congress on Services.

25.

Lin, C, Yuan, S, Leu, M., & Tsai, C. (2012). A framework for scalable cloud video recorder system in surveillance environment. In 2012 9th international conference on ubiquitous intelligence and computing and 9th international conference on autonomic and trusted computing.

26.

Kim, M. (2012). A Hadoop-based multimedia transcoding system for processing social media in the PaaS platform of SMCCSE. In KSII Transactions on Internet and Information Systems.

27.

Kim, M., Han, S., Yun, C., & Lee, H. (2014). Distributed multimedia streaming service algorithms over cloud computing environment. Future Information Engineering.

28.

Jokhio, F., Deneke, T., Lafond, S., & Lilius, J. (2012). Bit rate reduction video transcoding with distributed computing. In 2012 20th euromicro international conference on parallel, distributed and network-based processing. https://doi.org/10.1109/pdp.2012.59.

29.

Yu, Gu, Wang, Dongsheng, & Liu, Chuanyi. (2014). DR-Cloud: Multi-cloud based disaster recovery service. Tsinghua Science and Technology, 19(1), 13–23. https://doi.org/10.1109/tst.2014.6733204.CrossRef

30.

Wei, L., Cai, J., Foh, C., & He, B. (2017). QoS-aware resource allocation for video transcoding in clouds. IEEE Transactions on Circuits and Systems for Video Technology, 27(1), 49–61. https://doi.org/10.1109/tcsvt.2016.2589621.CrossRef

31.

Kesavaraja, D., & Shenbagavalli, A. (2017). QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2017.08.015.

32.

Storch, M., & Rose, C. (2017). Cloud storage cost modeling for cryptographic file systems. In 2017 25th euromicro international conference on parallel, distributed and network-based processing (PDP). https://doi.org/10.1109/pdp.2017.36.

33.

Al-Rawahi, M., Edirisinghe, E., & Jeyarajan, T. (2016). Machine learning-based framework for resource management and modelling for video analytic in cloud-based Hadoop environment. In 2016 Intl IEEE conferences on ubiquitous intelligence & computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld). https://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0128.

34.

Ma, Z., Teschendorff, A., Leijon, A., Qiao, Y., Zhang, H., & Guo, J. (2015). Variational Bayesian matrix factorization for bounded support data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(4), 876–889. https://doi.org/10.1109/tpami.2014.2353639.CrossRef

35.

Ma, Z., Xue, J., Leijon, A., Tan, Z., Yang, Z., & Guo, J. (2018). Decorrelation of neutral vector variables: Theory and applications. IEEE Transactions on Neural Networks and Learning Systems, 29(1), 129–143. https://doi.org/10.1109/tnnls.2016.2616445.MathSciNetCrossRef

36.

Mattmann, C., & Sharan, M. (2017). Scalable Hadoop-based pooled time series of big video data from the deep web. In Proceedings of the 2017 ACM on international conference on multimedia retrieval—ICMR ‘17. https://doi.org/10.1145/3078971.3079019.

37.

Son, S., & Kim, M. (2017). HVTS: Hadoop-based video transcoding system for media services. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. https://doi.org/10.1587/transfun.e100.a.1248.

38.

Taghia, J., Ma, Z., & Leijon, A. (2014). Bayesian estimation of the von-Mises Fisher mixture model with variational inference. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(9), 1701–1715. https://doi.org/10.1109/tpami.2014.2306426.CrossRef

Title: Framework for Fast and Efficient Cloud Video Transcoding System Using Intelligent Splitter and Hadoop MapReduce
Authors: D. Kesavaraja
A. Shenbagavalli
Publication date: 15-02-2018
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 3/2018
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-018-5501-3

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 3/2018

Optimization of Spatially-Coupled Multiuser Data Transmission Through Machine Learning Methods

Positive and Negative Link Prediction Algorithm Based on Sentiment Analysis in Large Social Networks

A Survey on Long Term Evolution Scheduling in Data Mining

Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering

Defect Prediction in Android Binary Executables Using Deep Neural Network

Distributed and Adaptive Analog Coding for Video Broadcast in Wireless Cooperative System