Energy- and Quality-Aware Video Request Policy for Wireless Adaptive Streaming Clients

Díaz, César; Fernández, A; Sacristan, Fernando; García, Narciso

doi:10.1109/tce.2020.3034619

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…Subsequently, in lines 18-19, the temperature difference of all cells with respect to the ambient temperature is calculated, where the linear difference is used to determine the convective heat loss (11) and the difference to the power of four is used for the radiative heat loss (9). In lines 21-22, the power flow due to heat conductance on the board is determined (10). In contrast // Power sum (6) Algorithm 1: Temperature-to-power conversion algorithm.…”

Section: Pixel-wise Power Modelmentioning

confidence: 99%

“…The goal is to extend operating times and to keep the overall energy consumption at a reasonable overall level. The energy consumption of such devices was assessed and optimized in many studies, for example for smartphone apps in general [1], [2], [3], in video coding [4], [5], [6], [7], video streaming [8], [9], [10], wearable consumer devices [11], or smart home applications [12], [13].…”

Section: Introductionmentioning

confidence: 99%

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Component-Wise Power Estimation of Electrical Devices Using Thermal Imaging

Herglotz

Grosche

Bharadwaj

et al. 2021

IEEE Trans. Consumer Electron.

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This paper presents a novel method to estimate the power consumption of distinct active components on an electronic carrier board by using thermal imaging. The components and the board can be made of heterogeneous material such as plastic, coated microchips, and metal bonds or wires, where a special coating for high emissivity is not required. The thermal images are recorded when the components on the board are dissipating power. In order to enable reliable estimates, a segmentation of the thermal image must be available that can be obtained by manual labeling, object detection methods, or exploiting layout information. Evaluations show that with low-resolution consumer infrared cameras and dissipated powers larger than 300 mW, mean estimation errors of 10% can be achieved.

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Section: Pixel-wise Power Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Component-Wise Power Estimation of Electrical Devices Using Thermal Imaging

Herglotz

Grosche

Bharadwaj

et al. 2021

IEEE Trans. Consumer Electron.

View full text Add to dashboard Cite

show abstract

“…The target of the videos' release focuses on the large-scale popularity over the internet [7][8][9][10]. The excellent content and convenient access attract mass users, but a large number of videos have short and small-scale popularity [11][12][13]. This is because the traditional video expansion mode uses a request-response method on overlay network limits to increase the scale of videos [14].…”

Section: Introductionmentioning

confidence: 99%

A Novel Video-Spreading Strategy Based on the Joint Estimation of Social Influence and Sharing Capacity in Wireless Networks

Jia

Liang

2023

Electronics

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In this paper, we propose a novel video-sharing strategy based on the joint estimation of social influence and sharing capacity in wireless networks (SSISC), which promotes the scale and efficiency of video spread and ensures the balance of supply and demand. SSISC designs an estimation model of video-sharing gains by investigating social influence levels, sharing capacities (including capacities of information dispatching and video delivery), and predicted expansion scale. Some social parameters (e.g., centrality of degree and betweenness, and average shortest distance) and some parameters of sharing performance (e.g., number of forwarded messages and cached videos, the average time of transmission and freeze) are used to evaluate social influence, capacities of information dispatching, and video delivery; video interest levels, social relationship levels, and historical push success rates are used to predict video proliferation scale. A video-spreading strategy based on the assistance of spread nodes is designed, which controls the process of video push according to available bandwidth and push priority to balance supply and demand and ensure user experience quality. Extensive tests show how SSISC achieves much better performance results in comparison with other state-of-the-art solutions.

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“…The development and application of wireless communication technologies (e.g., 5G/6G) not only support the ubiquitous access of mobile users to break time and space constraints, but also provide enough network bandwidth for various applications with a high requirement of bandwidth [1][2][3][4]. Video services such as IPTV, VoD, and video living rely on wonderful content and quality of services (QoS) and have become the most popular application on the Internet, so much so that video traffic has become more than 70% of the world's total traffic [5][6][7][8]. The combination between video applications and social networks promotes the process and range of video dissemination because the video spread considers social links between users as the new sharing channel [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Geo-Social-Aware Video Edge Delivery Strategy Based on Modeling of Social-Geographical Dynamic in an Urban Area

2022

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Social networks change the way and approaches of video spread and promote range and speed of video spread, which results in frequent traffic blowout and a heavy load on the networks. The social and geographical communication efficiency determines the efficiency of video sharing, which enables the eruptible traffic to be offloaded in underlaying networks to relieve the load of networks and ensure the user quality of the experience. In this paper, we propose a novel geo-social-aware video edge delivery strategy based on the modeling of the social-geographical dynamic in urban area (GSVD). By investigating the frequency of sharing behaviors, social communication efficiency, and efficiency of social sub-network consisting of one-hop social neighbors of users, GSVD estimates the interactive and basic social relationship to calculate the closeness of the social relationship between mobile users. GSVD makes use of grid partition and coding subarea to express the geographical location of mobile users and designs a calculation method of coding-based geographical distance. GSVD considers the dynamic update of social distance and geographical location and designs a measurement of video delivery quality in terms of delivery delay and playback continuity. A strategy of video delivery with the consideration of adapting to social-geographical dynamic is designed, which effectively promotes the efficiency of video sharing. Extensive tests show how GSVD achieves much better performance results in comparison with other state of the art solutions.

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Energy- and Quality-Aware Video Request Policy for Wireless Adaptive Streaming Clients

Cited by 6 publications

References 23 publications

Component-Wise Power Estimation of Electrical Devices Using Thermal Imaging

Component-Wise Power Estimation of Electrical Devices Using Thermal Imaging

A Novel Video-Spreading Strategy Based on the Joint Estimation of Social Influence and Sharing Capacity in Wireless Networks

A Novel Geo-Social-Aware Video Edge Delivery Strategy Based on Modeling of Social-Geographical Dynamic in an Urban Area

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