Smart mobile device power consumption measurement for video streaming in wireless environments: WiFi vs. LTE

2017 ◽  
Author(s):  
Longhao Zou ◽  
Ali Javed ◽  
Gabriel-Miro Muntean
Keyword(s):  
Power Consumption ◽  
Video Streaming ◽  
Mobile Device ◽  
Wireless Environments ◽  
Smart Mobile

Optimizing power consumption of mobile devices for video streaming over 4G LTE networks

2017 ◽  
Vol 11 (5) ◽  
pp. 1101-1114 ◽  
Author(s):  
Jingyu Zhang ◽  
Zhi-Jie Wang ◽  
Zhe Quan ◽  
Jian Yin ◽  
Yuanyi Chen ◽  
...  
Keyword(s):  
Power Consumption ◽  
Mobile Devices ◽  
Video Streaming ◽  
Lte Networks ◽  
4G Lte ◽  
4G Lte Networks

scholarly journals WiFi Transmit Power and its Effect on Co-Channel Interference

2021 ◽  
Vol 13 (1) ◽  
pp. 1-17
Author(s):  
Zyanya Cordoba ◽  
Riddhi Rana ◽  
Giovanna Rendon ◽  
Justin Thunell ◽  
Abdelrahman Elleithy
Keyword(s):  
Video Streaming ◽  
Multiple Access ◽  
Power Level ◽  
Control Algorithms ◽  
Transmit Power ◽  
Cooperative Approach ◽  
Channel Interference ◽  
High Bandwidth ◽  
Wireless Environments ◽  
Multiple Clients

The mass adoption of WiFi (IEEE 802.11) technology has increased numbers of devices simultaneously attempting to use high-bandwidth applications such as video streaming in a finite portion of the frequency spectrum. These increasing numbers can be seen in the deployment of highly-dense wireless environments in which performance can be affected due to the intensification of challenges such as co-channel interference (CCI). There are mechanisms in place to try to avoid sources of interference from non-WiFi devices. Still, CCI caused by legitimate WiFi traffic can be equally or even more disruptive, and also though some tools and protocols try to address CCI, these are no longer sufficient for this type of environment. Therefore, this paper investigates the effect of transmit power and direction have on CCI in a high-density environment consisting of multiple access points (APs) and multiple clients. We suggest improvements on publicly- existing documented power control algorithms and techniques by proposing a cooperative approach consisting of the incorporation of feedback from the receiver to the transmitter to allow it to reduce power level where possible, which will minimize the range of CCI for near clients without compromising coverage for the most distant ones.

scholarly journals Toward Cost-Effective Mobile Video Streaming through Environment-Aware Watching State Prediction

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3654 ◽  
Author(s):  
Xuanyu Wang ◽  
Weizhan Zhang ◽  
Xiang Gao ◽  
Jingyi Wang ◽  
Haipeng Du ◽  
...  
Keyword(s):  
Power Consumption ◽  
Video Streaming ◽  
Data Transmission ◽  
Physical Environment ◽  
Time Window ◽  
User Behavior ◽  
Cost Effective ◽  
Mobile Video ◽  
Data Usage ◽  
Mobile Video Streaming

Mobile video applications are becoming increasingly prevalent and enriching the way people learn and are entertained. However, on mobile terminals with inherently limited resources, mobile video streaming services consume too much energy and bandwidth, which is an urgent problem to solve. At present, research on cost-effective mobile video streaming typically focuses on the management of data transmission. Among such studies, some new approaches consider the user’s behavior to further optimize data transmission. However, these studies have not adequately discussed the specific impact of the physical environment on user behavior. Therefore, this paper takes into account the environment-aware watching state and proposes a cost-effective mobile video streaming scheme to reduce power consumption and mobile data usage. First, the watching state is predicted by machine learning based on user behavior and the physical environment during a given time window. Second, based on the resulting prediction, a downloading algorithm is introduced based on the user equipment (UE) running mode in the LTE system and the VLC player. Finally, according to the corresponding experimental results obtained in a real-world environment, the proposed approach, compared to its benchmarks, effectively reduces the data usage (14.4% lower than that of energy-aware, on average) and power consumption (about 19% when there are screen touches) of mobile devices.

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