A Cloud-Based Distributed Architecture to Accelerate Video Encoders
Source abstract
Abstract
Nowadays, video coding and transcoding have a great interest and important impact in areas such as high-definition video and entertainment, healthcare and elderly care, high-resolution video surveillance, self-driving cars, or e-learning. This growing demand for high-resolution video boosts the proposal of new codecs and the development of their encoders that require high computational requirements. Therefore, new strategies are needed to accelerate them. Cloud infrastructures offer interesting features for video coding, such as on-demand resource allocation, multitenancy, elasticity, and resiliency. This paper proposes a cloud-based distributed architecture, where the network and the storage layers have been tuned, to accelerate video encoders over an elastic number of worker encoder nodes. Moreover, an application is developed and executed in the proposed architecture to allow the creation of encoding jobs, their dynamic assignment, their execution in the worker encoder nodes, and the reprogramming of the failed ones. To validate the proposed architecture, the parallel execution of existing video encoders, x265 for H.265/HEVC and libvpx-vp9 for VP9, has been evaluated in terms of scalability, workload, and job distribution, varying the number of encoder nodes. The quality of the encoded videos has been analyzed for different bit rates and number of frames per job using the Peak Signal-to-Noise Ratio (PSNR). Results show that our proposal maintains video quality compared with the sequential encoding while improving encoding time, which can decrease near 90%, depending on the codec and the number of encoder nodes.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Video Coding and Compression Technologies · Image and Video Quality Assessment · Advanced Vision and Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departament d’Informàtica, Universitat de València, Avd. Universitat, SN, 46100 Burjassot, Spain
Funding
Agencia Estatal de Investigación · Generalitat Valenciana
References (31)
Show source-provided references
1. Sandvine (2020, March 10). Global Internet Phenomena Report. White Paper. Available online: https://www.sandvine.com.
2. Sandvine (2020, May 20). The COVID-19 Global Internet Phenomena Report. White Paper. Available online: https://www.sandvine.com/press-releases/sandvine-releases-covid-19-global-internet-phenomena-report.
3. (2020, March 10). Cisco Visual Networking Index: Forecast and Trends, 2017–2022. White Paper 1551296909190103, CISCO. Available online: https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html.
4. The MPEG-DASH Standard for Multimedia Streaming Over the Internet10.1109/MMUL.2011.71 ↗
5. Overview of the H.264/AVC video coding standard10.1109/TCSVT.2003.815165 ↗
6. Mukherjee, D., Bankoski, J., Grange, A., Han, J., Koleszar, J., Wilkins, P., Xu, Y., and Bultje, R. (2013, January 8–11). The latest open-source video codec VP9—An overview and preliminary results. Proceedings of the 2013 Picture Coding Symposium (PCS), San Jose, CA, USA.10.1109/PCS.2013.6737765 ↗
7. Overview of the High Efficiency Video Coding (HEVC) Standard10.1109/TCSVT.2012.2221191 ↗
8. Algorithm and Architecture Design of the H.265/HEVC Intra Encoder10.1109/TCSVT.2015.2428571 ↗
9. Rodríguez-Sánchez, R., Martínez, J.L., De Cock, J., Sánchez, J.L., Claver, J.M., and Van de Walle, R. (2013, January 15–18). Low delay H.264/AVC bidirectional inter prediction on a GPU. Proceedings of the 2013 IEEE International Conference on Image Processing, Melbourne, Australia.10.1109/ICIP.2013.6738435 ↗
10. Asif, M., Majeed, S., Taj, I.A., Bin Ahmed, M., and Ziauddin, S.M. (2014, January 10–13). Exploiting MB level parallelism in H.264/AVC encoder for multi-core platform. Proceedings of the 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), Doha, Qatar.10.1109/AICCSA.2014.7073188 ↗
11. Mell, P., and Grance, T. (2011). The NIST Definition of Cloud Computing (NIST Special Publication 800–145), NIST. Technical Report.10.6028/NIST.SP.800-145 ↗
12. A View of Cloud Computing10.1145/1721654.1721672 ↗
13. Amazon Web Services (2020, March 20). Netflix on AWS. Available online: https://aws.amazon.com/solutions/case-studies/netflix/.
14. Pereira, R., Azambuja, M., Breitman, K., and Endler, M. (2010, January 5–10). An Architecture for Distributed High Performance Video Processing in the Cloud. Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing, Miami, FL, USA.10.1109/CLOUD.2010.73 ↗
15. MapReduce-Based Distributed Video Encoding Using Content-Aware Video Segmentation and Scheduling10.1109/ACCESS.2016.2616540 ↗
16. Fouladi, S., Wahby, R.S., Shacklett, B., Balasubramaniam, K.V., Zeng, W., Bhalerao, R., Sivaraman, A., Porter, G., and Winstein, K. (2017, January 27–29). Encoding, Fast and Slow: Low-Latency Video Processing Using Thousands of Tiny Threads. Proceedings of the 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17), Boston, MA, USA. Available online: https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/fouladi.
17. Lao, F., Zhang, X., and Guo, Z. (2012, January 20–23). Parallelizing video transcoding using Map-Reduce-based cloud computing. Proceedings of the 2012 IEEE International Symposium on Circuits and Systems (ISCAS), Seoul, Korea.10.1109/ISCAS.2012.6271923 ↗
18. Towards efficient design and implementation of a hadoop-based distributed video transcoding system in cloud computing environment
19. Song, C., Shen, W., Sun, L., Lei, Z., and Xu, W. (2014, January 4–6). Distributed video transcoding based on MapReduce. Proceedings of the 2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS), Taiyuan, China.10.1109/ICIS.2014.6912152 ↗
20. Parallel video transcoding using Hadoop MapReduce
21. Elkabbany, G.F., and Moussa, M.M. (2020). Accelerating video encoding using cluster computing. Multimedia Tools and Applications, Springer.10.1007/s11042-020-08717-9 ↗
22. (2019, December 10). FFmpeg: A Complete, Cross-Platform Solution to Record, Convert and Stream Audio and Video. Available online: https://www.ffmpeg.org/.
23. MulticoreWare Inc (2019, December 10). x265 HEVC Encoder/H.265 Video Codec. Available online: https://bitbucket.org/multicoreware/x265/wiki/Home.
24. WebM Project (2019, December 10). The WebM VP8/VP9 Codec SDK. Available online: https://github.com/webmproject/libvpx/tree/master/vp9.
25. Blender Foundation (2019, December 10). Sintel, the Durian Open Movie Project. Available online: https://media.xiph.org.
26. Chirivella-Perez, E., Gutiérrez-Aguado, J., Claver, J.M., and Calero, J.M.A. (2015, January 26–28). Hybrid and Extensible Architecture for Cloud Infrastructure Deployment. Proceedings of the 2015 IEEE International Conference on Computer and Information Technology, Liverpool, UK.10.1109/CIT/IUCC/DASC/PICOM.2015.87 ↗
27. The Evolution of Video Quality Measurement: From PSNR to Hybrid Metrics10.1109/TBC.2008.2000733 ↗
28. Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, C.W., Andreopoulos, Y., and Breiteneder, C. (2012). A Fast GPU-Based Motion Estimation Algorithm for H.264/AVC. Advances in Multimedia Modeling, Springer.10.1007/978-3-642-27355-1 ↗
29. A Multi-Grained Parallel Solution for HEVC Encoding on Heterogeneous Platforms10.1109/TMM.2019.2916462 ↗
30. GPU-Based Hierarchical Motion Estimation for High Efficiency Video Coding10.1109/TMM.2018.2867260 ↗
31. Toward a transparent and efficient GPU cloudification architecture10.1007/s11227-018-2720-z ↗