QoE-Energy Consumption Optimization for End-User Devices in Adaptive Bitrate Video Streaming Using the Lagrange Multiplier Method
DOI:
https://doi.org/10.4108/eetinis.v12i3.8587Keywords:
Video streaming, Adaptive Bitrate, QoE, Energy consumptionAbstract
The reduction of greenhouse gas emissions in the Internet and ICT sectors has become a critical challenge. According to recent research, the key contributors to greenhouse gas emissions in Internet include high energy consumption factors such as data centers, transmission network devices, and end-user devices. Among Internet services, video streaming is one of the services having the highest traffic volume and number of users. Consequently, developing energy-efficient solutions for video streaming networks, particularly for end-user devices, is an urgent research priority. Reducing energy consumption in end-user devices in a video streaming system often requires compromises in parameters that impact the quality of user experience (QoE). Therefore, achieving an optimal trade-off between minimizing energy consumption and maintaining an acceptable QoE is a key objective. In this study, a cost function that integrates QoE and energy consumption is developed using the Lagrange multiplier method. Based on this function, an adaptive bitrate algorithm is proposed to select optimal video segments for video players, ensuring maximum QoE while minimizing energy consumption. The performance of the proposed method is evaluated using various types of video samples under varying network bandwidth conditions. Experimental results show that the proposed method reduces energy consumption of end-user devices by up to 6.7% and enhances QoE by 20% compared to previous methods.
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[1] Cacbon Truth, “Carbon impact of video streaming”, [Online]. Available: https://www.carbontrust.com/our-work-and-impact/guides-reports-and-tools/carbon-impact-of-video-streaming (last accessed : Dec. 2024).
[2] Ericsson. (2022)., “Mobile data traffic outlook,” [Online]. Available: https://www.ericsson.com/en/reports-and-papers/mobility-report/dataforecasts/mobile-traffic-forecast (last accessed : Dec. 2024).
[3] C. Bezerra, A. De Carvalho, D. Borges, N. Barbosa, J. Pontes and E. Tavares, "QoE and energy consumption evaluation of adaptive video streaming on mobile device," 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2017, pp. 1-6, doi: 10.1109/CCNC.2017.8016294.
[4] C. Herglotz, M. Kränzler, R. Schober, and A. Kaup, “Sweet Streams Are Made of This: The System Engineer’s View on Energy Efficiency in Video Communications [Feature],” IEEE Circuits Syst. Mag., vol. 23, no. 1, pp. 57–77, 2023, doi: 10.1109/mcas.2023.3234739.
[5] D. Silveira, M. Porto, and S. Bampi, “Performance and energy consumption analysis of the X265 video encoder,” 25th European Signal Processing Conference, EUSIPCO 2017, vol. 2017-Janua. pp. 1519–1523, 2017. doi: 10.23919/EUSIPCO.2017.8081463.
[6] L. Zou, A. Javed, and G.-M. Muntean, “Smart mobile device power consumption measurement for video streaming in wireless environments: WiFi vs. LTE,” in 2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2017, pp. 1–6. doi: 10.1109/BMSB.2017.7986151.
[7] X. Chen, T. Tan, G. Cao, and T. F. La Porta, “Context-Aware and Energy-Aware Video Streaming on Smartphones,” IEEE Trans. Mob. Comput., vol. 21, no. 3, pp. 862–877, 2022, doi: 10.1109/TMC.2020.3019341.
[8] K. Brunnström et al., Qualinet White Paper on Definitions of Quality of Experience. 2013.
[9] F. Dobrian et al., “Understanding the impact of video quality on user engagement,” SIGCOMM Comput. Commun. Rev., vol. 41, no. 4, pp. 362–373, Aug. 2011, doi: 10.1145/2043164.2018478.
[10] G. Bingol, S. Porcu, A. Floris, and L. Atzori, “An Analysis of the Trade-Off Between Sustainability and Quality of Experience for Video Streaming,” 2023, pp. 1600–1605. doi: 10.1109/ICCWorkshops57953.2023.10283614.
[11] HTTP Live Streaming, [Online]. Available: https://developer.apple.com/streaming/ (last accessed Dec., 2024).
[12] Microsoft Smooth Streaming, Online]. Available: https://learn.microsoft.com/en-us/openspecs/windows_protocols/ms-sstr/ (last accessed : Dec. 2024).
[13] Adobe HTTP Dynamic Streaming, [Online]. Available: https://helpx.adobe.com/adobe-media-server/dev/dynamic-streaming.html (last accessed : Dec. 2024).
[14] I. Sodagar, "The MPEG-DASH Standard for Multimedia Streaming Over the Internet," in IEEE Multi-Media, vol. 18, no. 4, pp. 62-67, April 2011, doi: 10.1109/MMUL.2011.71.
[15] M. Seufert, S. Egger, M. Slanina, T. Zinner, T. Hoßfeld, and P. Tran-Gia, “A Survey on Quality of Experience of HTTP Adaptive Streaming,” IEEE Commun. Surv. Tutorials, vol. 17, no. 1, pp. 469–492, 2015, doi: 10.1109/COMST.2014.2360940.
[16] Konstantoudakis, K., Breitgand, D., Doumanoglou, A. et al. Serverless streaming for emerging media: towards 5G network-driven cost optimization. Multimed Tools Appl 81, 12211–12250 (2022). doi.org/10.1007/s11042-020-10219-7.
[17] Y. Liu, S. Dey, F. Ulupinar, M. Luby, and Y. Mao, “Deriving and Validating User Experience Model for DASH Video Streaming,” IEEE Trans. Broadcast., vol. 61, no. 4, pp. 651–665, 2015, doi: 10.1109/TBC.2015.2460611.
[18] A. Mittal, A. K. Moorthy, and A. C. Bovik, “No-reference image quality assessment in the spatial domain,” IEEE Trans. Image Process., vol. 21, no. 12, pp. 4695–4708, 2012, doi: 10.1109/TIP.2012.2214050.
[19] X. Yin, A. Jindal, V. Sekar, and B. Sinopoli, “A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP,” in Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, 2015, pp. 325–338. doi: 10.1145/2785956.2787486.
[20] K. Spiteri, R. Urgaonkar, and R. K. Sitaraman, “BOLA: Near-Optimal Bitrate Adaptation for Online Videos,” IEEE/ACM Trans. Netw., vol. 28, no. 4, pp. 1698–1711, 2020, doi: 10.1109/TNET.2020.2996964.
[21] Zahaib Akhtar, Yun Seong Nam, Ramesh Govindan, Sanjay Rao, Jessica Chen, Ethan Katz-Bassett, Bruno Ribeiro, Jibin Zhan, and Hui Zhang. 2018. Oboe: autotuning video ABR algorithms to network conditions. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication (SIGCOMM ’18). Association for Computing Machinery, New York, NY, USA, 44–58. doi.org/10.1145/3230543.3230558.
[22] H. Mao, R. Netravali, and M. Alizadeh, “Neural Adaptive Video Streaming with Pensieve,” in Proceedings of the Conference of the ACM Special Interest Group on Data Communication, 2017, pp. 197–210. doi: 10.1145/3098822.3098843.
[23] M. Mu et al., “A Scalable User Fairness Model for Adaptive Video Streaming Over SDN-Assisted Future Networks,” IEEE J. Sel. Areas Commun., vol. 34, no. 8, pp. 2168–2184, 2016, doi: 10.1109/JSAC.2016.2577318.
[24] Schwenzer M., Ay M., Bergs T. et al., “Review on model predictive control: an engineering perspective,“ Int J Adv Manuf Technol 117, 1327–1349 (2021). doi.org/10.1007/s00170-021-07682-3.
[25] Arvind Narayanan, Xumiao Zhang, Ruiyang Zhu, Ahmad Hassan, Shuowei Jin, Xiao Zhu, Xiaoxuan Zhang, Denis Rybkin, Zhengxuan Yang, Zhuoqing Morley Mao, Feng Qian, and Zhi-Li Zhang, “A variegated look at 5G in the wild: performance, power, and QoE implications,” In Proceedings of the 2021 ACM SIGCOMM 2021 Conference (SIGCOMM ’21). Association for Computing Machinery, New York, NY, USA, 2021, 610–625. doi.org/10.1145/3452296.3472923
[26] S. Hao, D. Li, W. G. J. Halfond and R. Govindan, "Estimating mobile application energy consumption using program analysis," 2013 35th International Conference on Software Engineering (ICSE), San Francisco, CA, USA, 2013, pp. 92-101, doi: 10.1109/ICSE.2013.6606555.
[27] B. Varghese, G. Jourjon, K. Thilakarathne, and A. Seneviratne, “e-DASH: Modelling an energy-aware DASH player,” in 2017 IEEE 18th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2017, pp. 1–9. doi: 10.1109/WoWMoM.2017.7974320.
[28] S. Zhou, M. Ran, and Z. Lu, “Adaptive energy-efficient and QoE-aware optimization method for mobile video services,” in 2016 16th International Symposium on Communications and Information Technologies (ISCIT), 2016, pp. 388–392. doi: 10.1109/ISCIT.2016.7751657.
[29] D. Lorenzi, M. Nguyen, F. Tashtarian, and C. Timmerer, “E-WISH: An Energy-aware ABR Algorithm For Green HTTP Adaptive Video Streaming,” in Proceedings of the 3rd Mile-High Video Conference, 2024, pp. 28–33. doi: 10.1145/3638036.3640802.
[30] C. Herglotz, W. Robitza, M. Kränzler, A. Kaup and A. Raake, “Modeling of Energy Consumption and Streaming Video QoE using a Crowdsourcing Dataset,” 2022 14th International Conference on Quality of Multimedia Experience (QoMEX), Lippstadt, Germany, 2022, pp. 1-6, doi: 10.1109/QoMEX55416.2022.9900886.
[31] M. Ghasempour, H. Amirpour and C. Timmerer, “Real-Time Quality- and Energy-Aware Bitrate Ladder Construction for Live Video Streaming,” in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 15, no. 1, pp. 83-93, March 2025, doi: 10.1109/JETCAS.2025.3539948.
[32] Exoplayer, “Exoplayer”, [Online]. Available: https://developer.android.com/reference/androidx/media3/exoplayer/ExoPlayer (last accessed : Dec. 2024).
[33] Android, “BatteryManager”, [Online]. Available: https://source.android.com/docs/core/power/device (last accessed : Dec. 2024).
[34] “Linux Traffic Control”, [Online]. Available: https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/9 (last accessed : Dec. 2024).
[35] S. Lederer, C. Müller, and C. Timmerer, “Dynamic adaptive streaming over HTTP dataset,” in Proceedings of the 3rdMultimedia Systems Conference, 2012, pp. 89–94. doi: 10.1145/2155555.2155570.
[36] J. Vlaović, S. Rimac-Drlje and D. Žagar, “Influence of Segmentation Parameters on Video Quality in Dynamic Adaptive Streaming,” 2020 International Symposium ELMAR, Zadar, Croatia, 2020, pp. 37-40, doi: 10.1109/ELMAR49956.2020.9219029.
[37] G. Bjøntegaard, “Calculation of Average PSNR Differences between RD-curves,” 2001. [Online]. Available: https://api.semanticscholar.org/CorpusID:61598325 (last accessed: Dec. 2024).
[38] J. J. S. Pateux, “An Excel Add-in for Computing Bjontegaardmetric and its Evolution,” ITU-VCEG, VCEGAE07, vol. 2007, [Online]. Available: https://github.com/tbr/bjontegaard_etro (last accessed : Dec. 2024).
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