Internet-Scale Video Streaming over NDN

Ghasemi, Chavoosh; Yousefi, Hamed; Zhang, Beichuan

doi:10.1109/mnet.121.1900574

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…This research is particularly relevant for understanding how misinformation can affect public health behaviors and attitudes, emphasizing the need for accurate information dissemination during health crises. Ghasemi, Yousefi, and Zhang (2021) explore the technical aspects of internet use, specifically the efficiency and scalability of internet-scale video streaming over Named Data Networking (NDN). Their work contributes to the ongoing efforts to improve the technical infrastructure of the internet, ensuring that it can meet the growing demand for high-quality, scalable video content delivery.…”

Section: Literature Reviewmentioning

confidence: 99%

Regular Internet Users Across the Italian Regions

Leogrande

2024

SSRN Journal

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Section: Literature Reviewmentioning

confidence: 99%

Regular Internet Users Across the Italian Regions

Leogrande

2024

SSRN Journal

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“…In [18], the authors use a small physical testbed consisting of an Access Point and 20 Raspberry Pi-based Wi-Fi clients. Ghasemi et al [19] present a solution for adaptive video streaming service over NDN as a public ICN/NDN service for daily Internet users. They also show their deployment on the global NDN testbed.…”

Section: Related Workmentioning

confidence: 99%

Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments

2022

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The Information-Centric Network (ICN) paradigm has been touted as one of the candidates for the Internet of the future, where the Named Data Network (NDN) architecture is the one leading the way. Despite the large amount of works published in the literature targeting new implementations of such architecture, covering different network topologies and use cases, there are few NDN implementations in real networks. Moreover, most of these real-world NDN implementations, especially those addressing wireless and wired communication channels, are at a small scale, in laboratory environments. In this work, we evaluate the performance of an NDN-based implementation in a mobile wireless network, as part of a smart city infrastructure, making use of multiple wireless interfaces. We start by showing how we have implemented the NDN stack in current network nodes of the smart city infrastructure, following a hybrid solution where both TCP/IP and NDN paradigms can coexist. The implementation is evaluated in three scenarios, targeting different situations: mobility, the simultaneous use of different wireless interfaces and the network characteristics. The results show that our implementation works properly and insights about the correct NDN parameterization are derived.

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“…Numerous investigations have confirmed the feasibility of applying ICN in different domains of IoV applications [9]. Ghasemi et al [10] demonstrated video streaming over ICN/NDN on a global NDN (named data networking) test bed. Other studies [11,12] have investigated the potential of ICN using NDN with IEEE802.15.4 for constrained IoT devices to implement a real-world smart agriculture scenario.…”

Section: Introductionmentioning

confidence: 97%

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

et al. 2022

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Recently the Internet of Vehicles (IoV) has become a promising research area in the field of the Internet of Things (IoT), which enables vehicles to communicate and exchange real-time information with each other, as well as with infrastructure, people, and other sensors and actuators through various communication interfaces. The realization of IoV networks faces various communication and networking challenges to meet stringent requirements of low latency, dynamic topology, high data-rate connectivity, resource allocation, multiple access, and QoS. Advances in information-centric networks (ICN), edge computing (EC), and artificial intelligence (AI) will transform and help to realize the Intelligent Internet of Vehicles (IIoV). Information-centric networks have emerged as a paradigm promising to cope with the limitations of the current host-based network architecture (TCP/IP-based networks) by providing mobility support, efficient content distribution, scalability and security based on content names, regardless of their location. Edge computing (EC), on the other hand, is a key paradigm to provide computation, storage and other cloud services in close proximity to where they are requested, thus enabling the support of real-time services. It is promising for computation-intensive applications, such as autonomous and cooperative driving, and to alleviate storage burdens (by caching). AI has recently emerged as a powerful tool to break through obstacles in various research areas including that of intelligent transport systems (ITS). ITS are smart enough to make decisions based on the status of a great variety of inputs. The convergence of ICN and EC with AI empowerment will bring new opportunities while also raising not-yet-explored obstacles to realize Intelligent IoV. In this paper, we discuss the applicability of AI techniques in solving challenging vehicular problems and enhancing the learning capacity of edge devices and ICN networks. A comprehensive review is provided of utilizing intelligence in EC and ICN to address current challenges in their application to IIoV. In particular, we focus on intelligent edge computing and networking, offloading, intelligent mobility-aware caching and forwarding and overall network performance. Furthermore, we discuss potential solutions to the presented issues. Finally, we highlight potential research directions which may illuminate efforts to develop new intelligent IoV applications.

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Internet-Scale Video Streaming over NDN

Cited by 12 publications

References 8 publications

Regular Internet Users Across the Italian Regions

Regular Internet Users Across the Italian Regions

Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments

Convergence of Information-Centric Networks and Edge Intelligence for IoV: Challenges and Future Directions

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