Mediator-assisted multi-source routing in information-centric networks

Among the new communication paradigms recently proposed, information-centric networking (ICN) is able to natively support content awareness at the network layer shifting the focus from hosts (as in traditional IP networks) to information objects. In this paper, we exploit the intrinsic content-awareness ICN features to design a novel multi-source routing mechanism. It involves a new network entity, the ICN mediator, responsible for locating and delivering the requested information objects that are chunked and stored at different locations. Our approach imposes very limited signalling overhead, especially for large chunk size (MBytes). Simulations show significant latency reduction compared to traditional routing approaches

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues

Infocast: A New Paradigm for Collaborative Content Distribution from Roadside Units to Vehicular Networks Using Rateless Codes

In this paper, we address the problem of distributing a large amount of bulk data to a sparse vehicular network from roadside infostations, using efficient vehicle-to-vehicle collaboration. Due to the highly dynamic nature of the underlying vehicular network topology, we depart from architectures requiring centralized coordination, reliable MAC scheduling, or global network state knowledge, and instead adopt a distributed paradigm with simple protocols. In other words, we investigate the problem of reliable dissemination from multiple sources when each node in the network shares a limited amount of its resources for cooperating with others. By using \emph{rateless} coding at the Road Side Unit (RSU) and using vehicles as data carriers, we describe an efficient way to achieve reliable dissemination to all nodes (even disconnected clusters in the network). In the nutshell, we explore vehicles as mobile storage devices. We then develop a method to keep the density of the rateless codes packets as a function of distance from the RSU at the desired level set for the target decoding distance. We investigate various tradeoffs involving buffer size, maximum capacity, and the mobility parameter of the vehicles

Scalable Media Coding Enabling Content-Aware Networking

Increasingly popular multimedia services are expected to play a dominant role in the future of the Internet. In this context, it is essential that content-aware networking (CAN) architectures explicitly address the efficient delivery and processing of multimedia content. This article proposes the adoption of a content-aware approach into the network infrastructure, thus making it capable of identifying, processing, and manipulating media streams and objects in real time to maximize quality of service (QoS) and experience (QoE). Our proposal is built on the exploitation of scalable media coding technologies within such a content-aware networking environment. This discussion is based on four representative use cases for media delivery (unicast, multicast, peer-to-peer, and adaptive HTTP streaming) and reviews CAN challenges, specifically flow processing, caching/buffering, and QoS/QoE management

Liquid data networking

Accepted manuscrip

Enhancing the 3GPP V2X architecture with information-centric networking

Vehicle-to-everything (V2X) communications allow a vehicle to interact with other vehicles and with communication parties in its vicinity (e.g., road-side units, pedestrian users, etc.) with the primary goal of making the driving and traveling experience safer, smarter and more comfortable. A wide set of V2X-tailored specifications have been identified by the Third Generation Partnership Project (3GPP) with focus on the design of architecture enhancements and a flexible air interface to ensure ultra-low latency, highly reliable and high-throughput connectivity as the ultimate aim. This paper discusses the potential of leveraging Information-Centric Networking (ICN) principles in the 3GPP architecture for V2X communications. We consider Named Data Networking (NDN) as reference ICN architecture and elaborate on the specific design aspects, required changes and enhancements in the 3GPP V2X architecture to enable NDN-based data exchange as an alternative/complementary solution to traditional IP networking, which barely matches the dynamics of vehicular environments. Results are provided to showcase the performance improvements of the NDN-based proposal in disseminating content requests over the cellular network against a traditional networking solution119sem informaçãosem informaçã

Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery From Multiple Servers

In this paper, we deal with the problem of jointly determining the optimal coding strategy and the scheduling decisions when receivers obtain layered data from multiple servers. The layered data is encoded by means of prioritized random linear coding (PRLC) in order to be resilient to channel loss while respecting the unequal levels of importance in the data, and data blocks are transmitted simultaneously in order to reduce decoding delays and improve the delivery performance. We formulate the optimal coding and scheduling decisions problem in our novel framework with the help of Markov decision processes (MDP), which are effective tools for modeling adapting streaming systems. Reinforcement learning approaches are then proposed to derive reduced computational complexity solutions to the adaptive coding and scheduling problems. The novel reinforcement learning approaches and the MDP solution are examined in an illustrative example for scalable video transmission . Our methods offer large performance gains over competing methods that deliver the data blocks sequentially. The experimental evaluation also shows that our novel algorithms offer continuous playback and guarantee small quality variations which is not the case for baseline solutions. Finally, our work highlights the advantages of reinforcement learning algorithms to forecast the temporal evolution of data demands and to decide the optimal coding and scheduling decisions