24,202 research outputs found

    Random Linear Network Coding for 5G Mobile Video Delivery

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    An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.Comment: Invited paper for Special Issue "Network and Rateless Coding for Video Streaming" - MDPI Informatio

    Cellular-Broadcast Service Convergence through Caching for CoMP Cloud RANs

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    Cellular and Broadcast services have been traditionally treated independently due to the different market requirements, thus resulting in different business models and orthogonal frequency allocations. However, with the advent of cheap memory and smart caching, this traditional paradigm can converge into a single system which can provide both services in an efficient manner. This paper focuses on multimedia delivery through an integrated network, including both a cellular (also known as unicast or broadband) and a broadcast last mile operating over shared spectrum. The subscribers of the network are equipped with a cache which can effectively create zero perceived latency for multimedia delivery, assuming that the content has been proactively and intelligently cached. The main objective of this work is to establish analytically the optimal content popularity threshold, based on a intuitive cost function. In other words, the aim is to derive which content should be broadcasted and which content should be unicasted. To facilitate this, Cooperative Multi- Point (CoMP) joint processing algorithms are employed for the uni and broad-cast PHY transmissions. To practically implement this, the integrated network controller is assumed to have access to traffic statistics in terms of content popularity. Simulation results are provided to assess the gain in terms of total spectral efficiency. A conventional system, where the two networks operate independently, is used as benchmark.Comment: Submitted to IEEE PIMRC 201

    Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks

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    In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed.Comment: IEEE Communications Magazine, to appea
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