QoE Ready to Respond: A QoE-aware MEC Selection Scheme for DASH-based Adaptive Video Streaming to Mobile Users

Shi, Wanxin; Li, Qing; Zhang, Ruishan; Shen, Gengbiao; Jiang, Yong; Yuan, Zhenhui; Muntean, Gabriel‐Miro

doi:10.1145/3474085.3475325

Cited by 12 publications

(3 citation statements)

References 23 publications

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“…Furthermore, 1s run with a sampling frequency of 100 (or 10 ms time interval for generating logs) can provide 100 entries of throughput, thus producing adequate data for accurate throughput prediction. Many researchers have studied the performance of 5G in simulated platforms, such as ns-3 [9], [39], [40].…”

Section: Pilot Studymentioning

confidence: 99%

Improving UE Energy Efficiency Through Network-Aware Video Streaming Over 5G

Palit

Sen

Mondal³

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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Adaptive Bitrate (ABR) Streaming over the cellular networks has been well studied in the literature; however, existing ABR algorithms primarily focus on improving the end-users' Quality of Experience (QoE) while ignoring the resource consumption aspect of the underlying device. Consequently, proactive attempts to download video data to maintain the user's QoE often impact the battery life of the underlying device unless the download attempts are synchronized with the network's channel condition. In this work, we develop EnDASH-5G -a wrapper over the popular DASH-based ABR streaming algorithm, which establishes this synchronization by utilizing a network-aware video data download mechanism. EnDASH-5G utilizes a novel throughput prediction mechanism for 5G mmWave networks by upgrading the existing throughput prediction models with a transfer learning-based approach leveraging publicly available 5G datasets. It then exploits deep reinforcement learning to dynamically decide the playback buffer length and the video bitrate using the predicted throughput. This ensures that the data download attempts get synchronized with the underlying network condition, thus saving the device's battery power. From a thorough evaluation of EnDASH-5G, we observe that it achieves a near 30.5% decrease in the maximum energy consumption than the state-of-the-art Pensieve ABR algorithm while performing almost at par in QoE.

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Section: Pilot Studymentioning

confidence: 99%

Improving UE Energy Efficiency Through Network-Aware Video Streaming Over 5G

Palit

Sen

Mondal³

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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“…In addition, Electrochemical Impedance Model (EIM) [ 12 ] was developed. Electrochemical impedance spectroscopy (EIS) [ 13 ] is also commonly used as a model for estimating SOC. EIM and EIS both believe that there is a certain correspondence between battery impedance and SOC.…”

Section: Related Workmentioning

confidence: 99%

“…Equivalent Circuit Model (ECM) [ 13 – 15 ] is used to describe and simulate the dynamic characteristics, which are treated as a two-port network. Standard models include the Rint model, Thevenin model, and Partnership for a New Generation of Vehicle (PNGV) model [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

Li¹,

Jiang²,

Guo

et al. 2022

Computational Intelligence and Neuroscience

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The state of charge (SOC) is one of the main indexes of the lithium-ion battery, which affects the practice range of new energy vehicles and the safety of the battery. Nevertheless, the value of SOC cannot be measured directly. At present, the algorithm for estimating the state of charge is not very satisfactory. The multilayer perceptron algorithm designed during this paper encompasses a sensible impact on state estimation. During this paper, the multilayer network is designed to estimate the charged state of lithium batteries from the three-layer artificial neural network to the eleven-layer artificial neural network. After preprocessing the dataset and comparing several activation functions, the ten-layer fully connected neural network is the most efficient to estimate the SOC. In order to prevent over-fitting of the multilayer perceptron algorithm, the two techniques of the BatchNormalization layer and Dropout layer work together to inhibit over-fitting. At the same time, the accuracy of extended Kalman filter, long and short memory network, and recurrent neural network are compared. The multilayer perceptron network designed during this paper has the highest accuracy. Finally, in the open dataset, both the training and test errors achieve good results. The algorithm developed in this paper has made some progress in SOC estimation.

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