What is it about?
In this publication, we address the challenges faced by networks that connect multimedia vehicles to the internet. With the increasing number of internet-connected vehicles accessing real-time multimedia services, managing the network's resources optimally has become crucial. We propose a solution that dynamically adjusts the scale of resources based on demand, effectively reducing energy usage. Our approach also includes distributing multimedia traffic among servers, determining routes with high quality of service, and selecting media with high quality of experience. Through real-world testing, we have demonstrated the effectiveness of our solution in reducing the count of active servers and switches while enhancing various quality parameters.
Featured Image
Photo by Denny Müller on Unsplash
Why is it important?
Our work addresses the pressing need for efficient resource allocation in the context of multimedia streaming in Internet of Vehicles (IoV) networks. With the rapid expansion of multimedia applications and the integration of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV), our proposed framework, ELQ2, offers a timely and innovative solution. By dynamically managing server capacity and energy consumption, our approach aims to significantly improve the quality of multimedia services while optimizing resource usage. This work has the potential to make a substantial difference in the field of IoV networks and could be instrumental in shaping the future of multimedia streaming in vehicular communication networks.
Perspectives
As an individual, I find the publication "Efficient Resource Allocation for Multimedia Streaming in Software-Defined Internet of Vehicles" to be a significant and timely contribution to the field of Internet of Vehicles (IoV) networks. The study addresses the pressing challenges faced by IoV networks, such as the increasing growth of IoMV network and high-volume multimedia traffic, severe load variations during peak and non-peak hours, decline in Quality of Service (QoS) and Quality of Experience (QoE) during peak periods, and energy wastage during non-peak hours. The proposed ELQ2 architecture, designed for the integrated management of servers and switches within the IoMV network, offers a promising solution to optimize energy usage while providing users with high-quality multimedia streaming. The study's focus on the efficient management of resources, including energy, load, QoS, and QoE, is particularly relevant in the context of the rapid expansion of multimedia applications and the utilization of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) technologies in IoV. The modular architecture of ELQ2, combined with the use of SDN and NFV, allows for dynamic adjustment of resource allocation based on demand, effectively reducing energy usage and enhancing various QoS and QoE parameters. Overall, the publication provides valuable insights into the complex challenges faced by IoV networks and offers a comprehensive framework for addressing these challenges. It has the potential to significantly impact the field by improving the efficiency and sustainability of multimedia streaming in the context of the Internet of Vehicles.
AhmadReza Montazerolghaem
University of Isfahan
Read the Original
This page is a summary of: Efficient Resource Allocation for Multimedia Streaming in Software-Defined Internet of Vehicles, IEEE Transactions on Intelligent Transportation Systems, December 2023, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/tits.2023.3303404.
You can read the full text:
Contributors
The following have contributed to this page
