Scheduling Heuristics for Live Video Transcoding on Cloud Edges

doi:10.3969/j.issn.1673-5188.2017.02.005

ZTE Communications ›› 2017, Vol. 15 ›› Issue (2): 35-41.DOI: 10.3969/j.issn.1673-5188.2017.02.005

• Special Topic • Previous Articles Next Articles

Scheduling Heuristics for Live Video Transcoding on Cloud Edges

Panagiotis Oikonomou¹, Maria G. Koziri¹, Nikos Tziritas², Thanasis Loukopoulos¹, XU Cheng-Zhong²

¹ University of Thessaly, Lamia 35100, Greece
² Research Center for Cloud Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Received:2017-02-09 Online:2017-04-25 Published:2019-12-24
About author:Panagiotis Oikonomou (paikonom@uth.gr) received his Diploma degree (2008) and M.Sc. degree (2010) from the Department of Electrical and Computer Engineering, University of Thessaly, Greece. He is currently a Ph.D. candidate at the same Department. His research interests include optimization algorithms and fuzzy logic methods.|Maria G. Koziri (mkoziri@uth.gr) received her Diploma degree in computer engineering from the Technical University of Crete, Greece in 2003 and Ph.D. degree in computer science from the University of Thessaly, Greece in 2007. She is currently a visiting lecturer in the Computer Science Department of the University of Thessaly. Her research interests include video compression, scalable video coding, rate-distortion optimization and computer architecture.|Nikos Tziritas (nikolaos@siat.ac.cn) received his B.Sc. degree from the Technological Educational Institute of Serres, Greece in 2004, and M.Sc. and Ph.D. degrees from the University of Thessaly, Greece in 2006 and 2011, respectively. He is currently an associate professor in Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China. His work has appeared in over 35 publications. He is the recipient of the Award for Excellence for Early Career Researchers in Scalable Computing from IEEE Technical Committee in Scalable Computing in 2016.|Thanasis Loukopoulos (luke@dib.uth.gr) received his Ph.D. degree in computer science from the Hong Kong University of Science and Technology, China. He is currently a lecturer at the Department of Computer Science and Biomedical Informatics of the University of Thessaly, Greece. His research interests are in green computing, cloud computing, WSNs, scheduling, load balancing and video coding parallelization. His work appeared in over 50 publications. He had the best paper award in ICPP 2001.|XU Cheng-Zhong (cz.xu@siat.ac.cn) received the Ph.D. degree in computer science from the University of Hong Kong, China in 1993. He is currently a professor in the Department of Electrical and Computer Engineering of Wayne State University, China and the director of Cloud and Internet Computing Laboratory (CIC) and Sun’s Center of Excellence in Open Source Computing and Applications (OSCA). His research interest is mainly in scalable distributed and parallel systems and wireless embedded computing devices. He has published two books and more than 160 articles in peer-reviewed journals and conferences in these areas.

Abstract

Abstract:

Efficient video delivery involves the transcoding of the original sequence into various resolutions, bitrates and standards, in order to match viewers’ capabilities. Since video coding and transcoding are computationally demanding, performing a portion of these tasks at the network edges promises to decrease both the workload and network traffic towards the data centers of media providers. Motivated by the increasing popularity of live casting on social media platforms, in this paper we focus on the case of live video transcoding. Specifically, we investigate scheduling heuristics that decide on which jobs should be assigned to an edge mini-datacenter and which to a backend datacenter. Through simulation experiments with different QoS requirements we conclude on the best alternative.

Key words: video transcoding, edge computing, scheduling, heuristics, x264

Panagiotis Oikonomou, Maria G. Koziri, Nikos Tziritas, Thanasis Loukopoulos, XU Cheng-Zhong. Scheduling Heuristics for Live Video Transcoding on Cloud Edges[J]. ZTE Communications, 2017, 15(2): 35-41.

Figures/Tables 8

References 26

[1]	J. Gubbi, R Buyya, S. Marusic, and M. Palaniswami, “Internet of things (IoT): a vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, Sept. 2013. doi: 10.1016/j.future.2013.01. 010.
[2]	S. K. Khaitan and J. D. McCalley, “Design techniques and applications of cyber physical systems: a survey,” IEEE Systems Journal, vol. 9, no. 2, pp. 350-365, Jun. 2015. doi: 10.1109/JSYST.2014.2322503.
[3]	Cisco Systems Inc. (2017, Jan. 30). Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015-2020 [Online White Paper]. Available:
[4]	T. Wiegand, G. J. Sullivan, G. Bjøntegaard, A. Luthra , “Overview of the H.264/AVC video coding standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 7, pp.. 560-576, Jul. 2003. doi: 10.1109/TCSVT.2003.815165.
[5]	G. J. Sullivan, J.-R. Ohm, W.-J. Han, and T. Wiegand, “Overview of the high efficiency video coding (HEVC) standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp.. 1649-1668, Dec. 2012. doi: 10.1109/TCSVT.2012.2221191.
[6]	D. Grois, D. Marpe, A. Mulayoff , et al., “Performance comparison of H.265/MPEG-HEVC, VP9, and H.264/MPEG-AVC encoders,” in Picture Coding Symposium (PCS), San Jose, USA, Dec. 2013, pp. 394-397. doi: 10.1109/PCS. 2013.6737766.
[7]	R. A. Pardo, K. Pires, A. Blanc, G. Simon , “Transcoding live adaptive video streams at a massive scale in the cloud,” in ACM SIGMM Conference on Multimedia Systems (MMSys), Portland, USA, Mar. 2015, pp. 49-60. doi: 10.1145/2713168.2713177.
[8]	VideoLAN. ( 2017, Jan. 30). x264 home page [Online]. Available:
[9]	F. Bossen , “Common test conditions and software reference configurations,” JCT-VC, San Jose, USA, Document: JCTVC-H1100, Feb. 2012.
[10]	M. G. Koziri, P. Papadopoulos, N. Tziritas , et al., “Slice-based parallelization in HEVC encoding: realizing the potential through efficient load balancing,” in IEEE International Workshop on Multimedia Signal Processing (MMSP), Montreal, Canada, Sept. 2016, pp. 1-6. doi: 10.1109/MMSP.2016.7813354.
[11]	M. Shafique, M. U. K. Khan, and J. Henkel, “Power efficient and workload balanced tiling for parallelized high efficiency video coding,” in IEEE International Conference on Image Processing (ICIP), Paris, France, Oct. 2014, pp. 1253-1257. doi: 10.1109/ICIP.2014.7025250.
[12]	C. C. Chi, M. A. Mesa, B. Juurlink , et al., “Parallel scalability and efficiency of HEVC parallelization approaches,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 22, no. 12, pp.. 1827-1838, Dec. 2012. doi: 10.1109/TCSVT.2012.2223056.
[13]	Y.-J. Ahn, T.-J. Hwang, D.-G. Sim, and W.-J. Han, “Implementation of fast HEVC encoder based on SIMD and data-level parallelism,” EURASIP Journal Image and Video Processing, vol. 16, Dec. 2014. doi: 10.1186/1687-5281-2014-16.
[14]	M. G. Koziri, D. Zacharis, I. Katsavounidis, N. Bellas , “Implementation of the AVS video decoder on a heterogeneous dual-core SIMD processor,” IEEE Transactions on Consumer Electronics, vol. 57, no. 2, pp.. 673-681, May 2011. doi: 10.1109/TCE.2011.5955207.
[15]	J. F. Franche, and S. Coulombe, “Fast H.264 to HEVC transcoder based on post-order traversal of quadtree structure,” in IEEE International Conference on Image Processing (ICIP), Quebec, Canada, Sept. 2015, pp. 477-481. doi: 10.1109/ICIP.2015.7350844.
[16]	I. Ahmad, X. Wei, Y. Sun, Y.-Q. Zhang, “Video transcoding: an overview of various techniques and research issues,” IEEE Transactions on Multimedia, vol. 7, no. 5, pp.. 793-804, Oct. 2005. doi: 10.1109/TMM.2005.854472.
[17]	W. Zhang, Y. Wen, J. Cai, D. O. Wu , “Toward transcoding as a service in a multimedia cloud: energy-efficient job-dispatching algorithm,” IEEE Transactions on Vehicular Technology, vol. 63, no. 5, pp.. 2002-2012, Jun. 2014. doi: 10.1109/TVT.2014.2310394.
[18]	S. Lin, X. Zhang, Q. Yu , et al., “Parallelizing video transcoding with load balancing on cloud computing,” in IEEE International Symposium on Circuits and Systems (ISCAS), Beijing, China, May 2013, pp. 2864-2867. doi: 10.1109/ISCAS.2013.6572476.
[19]	A. Ashraf, F. Jokhio, T. Deneke , et al., “Stream-based admission control and scheduling for video transcoding in cloud computing,” in IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), Delft, Netherlands, May 2013, pp. 482-489. doi: 10.1109/CCGrid.2013.21.
[20]	G. Gao, W. Zhang, Y. Wen , et al., “Towards cost-efficient video transcoding in media cloud: insights learned from user viewing patterns,” IEEE Transactions on Multimedia, vol. 17, no. 8, pp.. 1286-1296, Aug. 2015. doi: 10.1109/TMM.2015.2438713.
[21]	S. U. R. Malik, S. U. Khan, S. J. Ewen, et al., “Performance analysis of data Intensive cloud systems based on data management and replication: a survey,” Distributed and Parallel Databases, vol. 34, no. 2, pp.. 179-215, Jun. 2016. doi: 10.1007/s10619-015-7173-2.
[22]	W. Ji, Z. Li, Y. Chen , “Joint source-channel coding and optimization for layered video broadcasting to heterogeneous devices,” IEEE Transactions on Multimedia, vol. 14, no. 2, pp.. 443-455, Apr. 2012. doi: 10.1109/TMM.2011.2177645.
[23]	W. Ji, Z. Li, Y. Chen , “Content-aware utility-fair video streaming in wireless broadcasting networks,” in IEEE International Conference on Image Processing (ICIP), Brussels, Belgium, Sept. 2011, pp. 145-148. doi: 10.1109/ICIP.2011.6115717.
[24]	M. T. Beck, S. Feld, A. Fichtner , et al., “ME-VoLTE: network functions for energy-efficient video transcoding at the mobile edge,” in International Conference on Intelligence in Next Generation Networks (ICIN), Paris, France, Feb. 2015, pp. 38-44. doi: 10.1109/ICIN.2015.7073804.
[25]	J. De Cock, A. Mavlankar, A. Moorthy, A. Aaron , “A large-scale video codec comparison of x264, x265 and libvpx for practical VOD applications,” SPIE Applications of Digital Image Processing XXXIX, vol. 9971, 997116. Sept. 2016. doi: 10.1117/12.2238495.
[26]	Amazon Web Services . ( 2017, Jan. 30). Amazon EC2 Instance Types [Online]. Available:

Name	Value
Dataset duration	75,079 s
Average broadcast duration	23,263.4 s
Max broadcast duration	1.05E6 s
Number of broadcasts	786,100
Total transcoding tasks	1,244,450
Percentage of braodcasts at 1080p	26.21%
Percentage of braodcasts at 720p	53.24%
Percentage of braodcasts at 480p	11.18%
Percentage of braodcasts at 360p	7.49%
Percentage of braodcasts at 240p	1.86%

Name	Value
Dataset duration	75,079 s
Average broadcast duration	23,263.4 s
Max broadcast duration	1.05E6 s
Number of broadcasts	786,100
Total transcoding tasks	1,244,450
Percentage of braodcasts at 1080p	26.21%
Percentage of braodcasts at 720p	53.24%
Percentage of braodcasts at 480p	11.18%
Percentage of braodcasts at 360p	7.49%
Percentage of braodcasts at 240p	1.86%

Name	Resolution	Frames	Time 240p (fps)	Time 360p (fps)	Time 480p (fps)	Time 720p (fps)
BasketballDrive	1920×1080	500	15.78	5.37	3.78	1.47
BQTerrace	1920×1080	600	29.10	9.11	6.42	2.37
Cactus	1920×1080	500	25.48	8.77	5.09	1.96
Kimono	1920×1080	240	18.54	6.05	4.33	1.62
ParkScene	1920×1080	240	24.95	7.62	5.28	1.94
PeopleOnStreet	2560×1600	150	10.01	2.82	2.00	0.74
Traffic	2560×1600	150	27.36	8.06	5.77	2.17
Average	-	-	21.60	6.82	4.66	1.75
Weights	-	-	1.00	3.16	4.63	12.34

Name	Resolution	Frames	Time 240p (fps)	Time 360p (fps)	Time 480p (fps)	Time 720p (fps)
BasketballDrive	1920×1080	500	15.78	5.37	3.78	1.47
BQTerrace	1920×1080	600	29.10	9.11	6.42	2.37
Cactus	1920×1080	500	25.48	8.77	5.09	1.96
Kimono	1920×1080	240	18.54	6.05	4.33	1.62
ParkScene	1920×1080	240	24.95	7.62	5.28	1.94
PeopleOnStreet	2560×1600	150	10.01	2.82	2.00	0.74
Traffic	2560×1600	150	27.36	8.06	5.77	2.17
Average	-	-	21.60	6.82	4.66	1.75
Weights	-	-	1.00	3.16	4.63	12.34

Scheduling Heuristics for Live Video Transcoding on Cloud Edges

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 26

Related Articles 15

Recommended Articles

Metrics

[1]	ZHOU Yiheng, ZENG Wei, ZHENG Qingfang, LIU Zhilong, CHEN Jianping. A Survey on Task Scheduling of CPU-GPU Heterogeneous Cluster [J]. ZTE Communications, 2024, 22(3): 83-90.
[2]	GAO Yuehong, NING Zhi, HE Jia, ZHOU Jinfei, GAO Chenqiang, TANG Qingkun, YU Jinghai. Review on Service Curves of Typical Scheduling Algorithms [J]. ZTE Communications, 2024, 22(2): 55-70.
[3]	AWADA Uchechukwu, ZHANG Jiankang, CHEN Sheng, LI Shuangzhi, YANG Shouyi. Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing [J]. ZTE Communications, 2023, 21(2): 40-52.
[4]	CAO Yinfeng, CAO Jiannong, WANG Yuqin, WANG Kaile, LIU Xun. Security in Edge Blockchains: Attacks and Countermeasures [J]. ZTE Communications, 2022, 20(4): 3-14.
[5]	ZHAO Kongyange, GAO Bin, ZHOU Zhi. Cost-Effective Task Scheduling for Collaborative Cross-Edge Analytics [J]. ZTE Communications, 2021, 19(2): 11-19.
[6]	SHI Wenqi, SUN Yuxuan, HUANG Xiufeng, ZHOU Sheng, NIU Zhisheng. Scheduling Policies for Federated Learning in Wireless Networks: An Overview [J]. ZTE Communications, 2020, 18(2): 11-19.
[7]	WU Hequan. Ten Reflections on 5G [J]. ZTE Communications, 2020, 18(1): 1-4.
[8]	Mohammed SEID, Stephen ANOKYE, SUN Guolin. Machine Learning Based Unmanned Aerial Vehicle Enabled Fog-Radio Aerial Vehicle Enabled Fog-Radio Access Network and Edge Computing [J]. ZTE Communications, 2019, 17(4): 33-45.
[9]	CAO Jie, XU Lanyu, Raef Abdallah, SHI Weisong. An OS for Internet of Everything: Early Experience from A Smart Home Prototype [J]. ZTE Communications, 2017, 15(4): 12-22.
[10]	ZHOU Yuezhi, ZHANG Di, ZHANG Yaoxue. A Transparent and User-Centric Approach to Unify Resource Management and Code Scheduling of Local, Edge, and Cloud [J]. ZTE Communications, 2017, 15(4): 3-11.
[11]	TU Yaofeng, DONG Zhenjiang, YANG Hongzhang. Key Technologies and Application of Edge Computing [J]. ZTE Communications, 2017, 15(2): 26-34.
[12]	Borui Ren, Gang Liu, Bin Hou. Utility-Based Joint Scheduling Approach Supporting Multiple Services for CoMP-SU-MIMO in LTE-A System [J]. ZTE Communications, 2015, 13(1): 60-66.
[13]	Tung Nguyen and Weisong Shi. MapReduce in the Cloud: Data-Location-Aware VM Scheduling [J]. ZTE Communications, 2013, 11(4): 18-26.
[14]	Shih-Hao Hung, Tei-Wei Kuo, Chi-Sheng Shih, Jeng-Peng Shieh, Chen-Pang Lee, Che-Wei Chang, and Jie-Wen Wei. A Cloud-Based Virtualized Execution Environment for Mobile Applications [J]. ZTE Communications, 2011, 9(1): 15-21.
[15]	Liu Fuqiang, Shan Lianhai. Heterogeneous Vehicular Communication Architecture and Key Technologies [J]. ZTE Communications, 2010, 8(4): 39-44.