Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Scaling01:26

Scaling

271
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
271
Distributed Loads01:19

Distributed Loads

555
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
555
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

85
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
85
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

95
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
95
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

668
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
668
IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

12.2K
Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and...
12.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Federated Reinforcement Learning-Based Dynamic Resource Allocation and Task Scheduling in Edge for IoT Applications.

Sensors (Basel, Switzerland)·2025
Same author

Tiny Language Models for Automation and Control: Overview, Potential Applications, and Future Research Directions.

Sensors (Basel, Switzerland)·2025
Same author

Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques.

Sensors (Basel, Switzerland)·2025
Same author

A Novel 3D Reversible Data Hiding Scheme Based on Integer-Reversible Krawtchouk Transform for IoMT.

Sensors (Basel, Switzerland)·2023
Same author

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data.

Sensors (Basel, Switzerland)·2023
Same author

Multiscale Feature-Learning with a Unified Model for Hyperspectral Image Classification.

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Jul 16, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

9.9K

A Scalable Video Multicast Scheme Based on User Demand Perception and D2D Communication.

Ruiqi Ouyang1, Xuanrui Xiong1, Mingkai Fu1

  • 1School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

This study introduces a new scalable video multicast scheme using Device-to-Device (D2D) communication to improve wireless video transmission. The method enhances network performance by analyzing user demand and optimizing cluster formation for better video quality.

Keywords:
D2D communicationmulticastscalable videospectrum sharingvideo recommendation

More Related Videos

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

598

Related Experiment Videos

Last Updated: Jul 16, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

9.9K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.1K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

598

Area of Science:

  • Wireless communication networks
  • Multimedia systems
  • Network engineering

Background:

  • The proliferation of 5G technology and smart devices has led to a surge in wireless video demand and traffic.
  • Complex networks, heterogeneous channels, and growing traffic pose challenges for wireless video applications.
  • Scalable video coding improves efficiency, but traditional base stations face limitations in handling large-scale video transmissions.

Purpose of the Study:

  • To propose a scalable video multicast scheme leveraging user demand perception and Device-to-Device (D2D) communication.
  • To enhance the D2D multicast network transmission performance for scalable videos within cellular D2D hybrid networks.
  • To address the limitations of traditional base stations in managing simultaneous video transmissions for numerous users.

Main Methods:

  • Analyzing user interests through viewing history and video popularity to gauge willingness for video pushing.
  • Implementing a cluster head selection algorithm considering channel quality, social parameters, and video quality requirements.
  • Developing a scheme for scalable video multicast in cellular D2D hybrid networks.

Main Results:

  • The proposed scheme effectively attracts users to join multicast clusters based on perceived demand.
  • It successfully increases the number of users participating in multicast clusters.
  • The scheme demonstrates the ability to meet diverse user demands for video quality.

Conclusions:

  • The developed scalable video multicast scheme improves D2D network transmission performance in hybrid networks.
  • User demand perception and optimized cluster head selection are key to enhancing multicast efficiency.
  • This approach effectively addresses the challenges of large-scale wireless video delivery in 5G environments.