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CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

Published: 16 December 2022 Publication History

Abstract

The edge computing paradigm brings cloud capabilities close to the clients. Leveraging the edge&#x2019;s capabilities can improve video streaming services by employing the storage capacity and processing power at the edge for caching and transcoding tasks, respectively, resulting in video streaming services with higher quality and lower latency. In this paper, we propose CD-LwTE, a <inline-formula> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula>ost- and <inline-formula> <tex-math notation="LaTeX">${D}$ </tex-math></inline-formula>elay-aware <inline-formula> <tex-math notation="LaTeX">${L}$ </tex-math></inline-formula>ight-<inline-formula> <tex-math notation="LaTeX">${w}$ </tex-math></inline-formula>eight <inline-formula> <tex-math notation="LaTeX">${T}$ </tex-math></inline-formula>ranscoding approach at the <inline-formula> <tex-math notation="LaTeX">${E}$ </tex-math></inline-formula>dge, in the context of HTTP Adaptive Streaming (HAS). The encoding of a video segment requires computationally intensive search processes. The main idea of CD-LwTE is to store the optimal search results as metadata for each bitrate of video segments and reuse it at the edge servers to reduce the required time and computational resources for transcoding. Aiming at minimizing the cost and delay of Video-on-Demand (VoD) services, we formulate the problem of selecting an optimal policy for serving segment requests at the edge server, including (<inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>) storing at the edge server, (ii) transcoding from a higher bitrate at the edge server, and (iii) fetching from the origin or a CDN server, as a Binary Linear Programming (BLP) model. As a result, CD-LwTE stores the popular video segments at the edge and serves the unpopular ones by transcoding using metadata or fetching from the origin/CDN server. In this way, in addition to the significant reduction in bandwidth and storage costs, the transcoding time of a requested segment is remarkably decreased by utilizing its corresponding metadata. Moreover, we prove the proposed BLP model is an NP-hard problem and propose two heuristic algorithms to mitigate the time complexity of CD-LwTE. We investigate the performance of CD-LwTE in comprehensive scenarios with various video contents, encoding software, encoding settings, and available resources at the edge. The experimental results show that our approach (<inline-formula> <tex-math notation="LaTeX">${i}$ </tex-math></inline-formula>) reduces the transcoding time by up to 97&#x0025;, (ii) decreases the streaming cost, including storage, computation, and bandwidth costs, by up to 75&#x0025;, and (iii) reduces delay by up to 48&#x0025; compared to state-of-the-art approaches.

References

[1]
M. Cha, H. Kwak, P. Rodriguez, Y.-Y. Ahn, and S. Moon, “Analyzing the video popularity characteristics of large-scale user generated content systems,” IEEE/ACM Trans. Netw., vol. 17, no. 5, pp. 1357–1370, Oct. 2009.
[2]
T. X. Tran, P. Pandey, A. Hajisami, and D. Pompili, “Collaborative multi-bitrate video caching and processing in mobile-edge computing networks,” in Proc. 13th Annu. Conf. Wireless On-demand Netw. Syst. Services (WONS), 2017, pp. 165–172.
[3]
T. X. Tran and D. Pompili, “Adaptive bitrate video caching and processing in mobile-edge computing networks,” IEEE Trans. Mobile Comput., vol. 18, no. 9, pp. 1965–1978, Sep. 2019.
[4]
H. Zhao, Q. Zheng, W. Zhang, B. Du, and H. Li, “A segment-based storage and transcoding trade-off strategy for multi-version VoD systems in the cloud,” IEEE Trans. Multimedia, vol. 19, no. 1, pp. 149–159, Jan. 2017.
[5]
A. Erfanian, H. Amirpour, F. Tashtarian, C. Timmerer, and H. Hellwagner, “LwTE: Light-weight transcoding at the edge,” IEEE Access, vol. 9, pp. 112276–112289, 2021.
[6]
F. Jokhio, A. Ashraf, S. Lafond, and J. Lilius, “A computation and storage trade-off strategy for cost-efficient video transcoding in the cloud,” in Proc. 39th Euromicro Conf. Softw. Eng. Adv. Appl., 2013, pp. 365–372.
[7]
G. Gao, W. Zhang, Y. Wen, Z. Wang, and W. Zhu, “Towards cost-efficient video transcoding in media cloud: Insights learned from user viewing patterns,” IEEE Trans. Multimedia, vol. 17, no. 8, pp. 1286–1296, Aug. 2015.
[8]
G. Gao, H. Hu, Y. Wen, and C. Westphal, “Resource provisioning and profit maximization for transcoding in clouds: A two-timescale approach,” IEEE Trans. Multimedia, vol. 19, no. 4, pp. 836–848, Apr. 2017.
[9]
Q. Jia, R. Xie, H. Lu, W. Zheng, and H. Luo, “Joint optimization scheme for caching, transcoding and bandwidth in 5G networks with mobile edge computing,” in Proc. IEEE 5th Int. Conf. Comput. Commun. (ICCC), 2019, pp. 999–1004.
[10]
Z. Zhang, R. Wang, F. R. Yu, F. Fu, and Q. Yan, “QoS aware transcoding for live streaming in edge-clouds aided hetnets: An enhanced actor-critic approach,” IEEE Trans. Veh. Technol., vol. 68, no. 11, pp. 11295–11308, Nov. 2019.
[11]
H. A. Pedersen and S. Dey, “Enhancing mobile video capacity and quality using rate adaptation, RAN caching and processing,” IEEE/ACM Trans. Netw., vol. 24, no. 2, pp. 996–1010, Apr. 2016.
[12]
T. Zhang and S. Mao, “Joint video caching and processing for multi-bitrate videos in ultra-dense HetNets,” IEEE Open J. Commun. Soc., vol. 1, pp. 1230–1243, 2020.
[13]
Y. Hao, L. Hu, Y. Qian, and M. Chen, “Profit maximization for video caching and processing in edge cloud,” IEEE J. Sel. Areas Commun., vol. 37, no. 7, pp. 1632–1641, Jul. 2019.
[14]
K. Bilal, E. Baccour, A. Erbad, A. Mohamed, and M. Guizani, “Collaborative joint caching and transcoding in mobile edge networks,” J. Netw. Comput. Appl., vol. 136, pp. 86–99, Jun. 2019.
[15]
LTE; Evolved Universal Terrestrial Radio Access Network (E-UTRAN); X2 General Aspects and Principles, 3GPP Standard Version 15.2.0 Release 15, Jan. 2020.
[16]
D. Lee, J. Lee, and M. Song, “Video quality adaptation for limiting transcoding energy consumption in video servers,” IEEE Access, vol. 7, pp. 126253–126264, 2019.
[17]
L. Li, D. Shi, R. Hou, R. Chen, B. Lin, and M. Pan, “Energy-efficient proactive caching for adaptive video streaming via data-driven optimization,” IEEE Internet Things J., vol. 7, no. 6, pp. 5549–5561, Jun. 2020.
[18]
F. Tashtarian, A. Erfanian, and A. Varasteh, “S2VC: An SDN-based framework for maximizing QoE in SVC-based HTTP adaptive streaming,” Comput. Netw., vol. 146, pp. 33–46, Dec. 2018.
[19]
A. Erfanian, F. Tashtarian, and M. H. Yaghmaee, “On maximizing QoE in AVC-based HTTP adaptive streaming: An SDN approach,” in Proc. IEEE/ACM 26th Int. Symp. Qual. Service (IWQoS), 2018, pp. 1–10.
[20]
A. Erfanian, F. Tashtarian, C. Timmerer, and H. Hellwagner, “QoCoVi: QoE-and cost-aware adaptive video streaming for the Internet of Vehicles,” Comput. Commun., vol. 190, pp. 1–9, Jun. 2022.
[21]
F. Tashtarian, A. Bentaleb, A. Erfanian, H. Hellwagner, C. Timmerer, and R. Zimmermann, “HxL3: Optimized delivery architecture for HTTP low-latency live streaming,” IEEE Trans. Multimedia, early access, Feb. 7, 2022. 10.1109/TMM.2022.3148587.
[22]
A. Erfanian, F. Tashtarian, R. Farahani, C. Timmerer, and H. Hellwagner, “On optimizing resource utilization in AVC-based real-time video streaming,” in Proc. 6th IEEE Int. Conf. Netw. Softwarization (NetSoft), Ghent, Belgium, Jun. 2020, pp. 301–309.
[23]
A. Erfanian, F. Tashtarian, A. Zabrovskiy, C. Timmerer, and H. Hellwagner, “OSCAR: On optimizing resource utilization in live video streaming,” IEEE Trans. Netw. Service Manag., vol. 18, no. 1, pp. 552–569, Mar. 2021.
[24]
Y. Jin, Y. Wen, and C. Westphal, “Optimal transcoding and caching for adaptive streaming in media cloud: An analytical approach,” IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 12, pp. 1914–1925, Dec. 2015.
[25]
Z. Wang, L. Sun, C. Wu, W. Zhu, Q. Zhuang, and S. Yang, “A joint online transcoding and delivery approach for dynamic adaptive streaming,” IEEE Trans. Multimedia, vol. 17, no. 6, pp. 867–879, Jun. 2015.
[26]
V. Veillon, C. Denninnart, and M. A. Salehi, “F-FDN: Federation of fog computing systems for low latency video streaming,” in Proc. IEEE 3rd Int. Conf. Fog Edge Comput. (ICFEC), 2019, pp. 1–9.
[27]
D. K. Krishnappa, M. Zink, and R. K. Sitaraman, “Optimizing the video transcoding workflow in content delivery networks,” in Proc. 6th ACM Multimedia Syst. Conf., 2015, pp. 37–48.
[28]
W. Li, S. M. Oteafy, and H. S. Hassanein, “Performance comparison of Transcoding and bitrate-aware caching in adaptive video streaming,” in Proc. IEEE Int. Conf. Commun. (ICC), 2019, pp. 1–7.
[29]
G. Gao and Y. Wen, “Video transcoding for adaptive bitrate streaming over edge-cloud continuum,” Digit. Commun. Netw., vol. 7, no. 4, pp. 598–604, 2021.
[30]
X. Li, M. A. Salehi, Y. Joshi, M. K. Darwich, B. Landreneau, and M. Bayoumi, “Performance analysis and modeling of video transcoding using heterogeneous cloud services,” IEEE Trans. Parallel Distrib. Syst., vol. 30, no. 4, pp. 910–922, Apr. 2019.
[31]
A. Erfanian, H. Amirpour, F. Tashtarian, C. Timmerer, and H. Hellwagner, “LwTE-live: Light-weight Transcoding at the edge for live streaming,” in Proc. Workshop Design, Deployment, Eval. Netw.-Assist. Video Streaming, 2021, pp. 22–28. [Online]. Available: https://doi.org/10.1145/3488662.3493829
[32]
E. Çetinkaya, H. Amirpour, M. Ghanbari, and C. Timmerer, “CTU depth decision algorithms for HEVC: A survey,” Signal Process. Image Commun., vol. 99, Nov. 2021, Art. no. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0923596521002113
[33]
H. Amirpour, M. Ghanbari, A. Pinheiro, and M. Pereira, “Motion estimation with chessboard pattern prediction strategy,” Multimedia Tools Appl., vol. 78, no. 15, pp. 21785–21804, Aug. 2019. [Online]. Available: https://doi.org/10.1007/s11042-019-7432-8
[34]
L. Cherkasova and M. Gupta, “Analysis of enterprise media server workloads: Access patterns, locality, content evolution, and rates of change,” IEEE/ACM Trans. Netw., vol. 12, no. 5, pp. 781–794, Oct. 2004.
[35]
S. Martello and P. Toth, “Algorithms for knapsack problems,” in Surveys in Combinatorial Optimization (North-Holland Mathematics Studies), vol. 132. Amsterdam, The Netherlands: North-Holland, 1987, pp. 213–257. [Online]. Available: https://doi.org/10.1016/S0304-0208(08)73237-7
[36]
J. Erickson, “NP-hard problems,” Algorithms Course Math., Lecture 21, Erickson, Portland, OR, USA, 2009.
[37]
S. Lloyd, “Least squares quantization in PCM,” IEEE Trans. Inf. Theory, vol. 28, no. 2, pp. 129–137, Mar. 1982.
[38]
Kmeans1d 0.3.1.” Accessed: Nov. 19, 2021. [Online]. Available: https://pypi.org/project/kmeans1d/
[39]
[40]
AWS pricing calculator estimate the cost for your architecture solution.” Accessed: Oct. 30, 2021. [Online]. Available: https://calculator.aws/
[41]
T. Wiegand, G. J. Sullivan, G. Bjontegaard, and A. Luthra, “Overview of the H.264/AVC video coding standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560–576, Jul. 2003.
[42]
G. J. Sullivan, J. Ohm, W. Han, and T. Wiegand, “Overview of the high efficiency video coding (HEVC) standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 22, no. 12, pp. 1649–1668, Dec. 2012.
[43]
V12.8: User’s Manual for CPLEX, Int. Bus. Mach. Corp., Armonk, NY, USA, 2017.

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            cover image IEEE Transactions on Network and Service Management
            IEEE Transactions on Network and Service Management  Volume 20, Issue 3
            Sept. 2023
            1837 pages

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            Published: 16 December 2022

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            • (2024)Lightweight Multitask Learning for Robust JND Prediction Using Latent Space and Reconstructed FramesIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.338998834:9(8657-8671)Online publication date: 1-Sep-2024

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