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Optimal Video Delivery in Mobile Networks Using a Cache-Accelerated Multi Area eMBMS Architecture

Abstract : Long-Term Evolution (LTE) evolved into enhanced Multimedia Broadcast/Multicast Service (eMBMS) that features improved perfomance, higher and more flexible LTE bit rates, Single Frequency Network (SFN) operations and carrier configuration flexibility. Multiple eMBMS service areas allow for efficient spectrum utilization in the context of mobile Content-Delivery-Networks (CDNs) as well as for delivering broadcast and push media over modern broadband networks. This paper intends to highlight novel service architectures for efficient content delivery. Content caching is a widely used technique in the networking industry that brings content closer to end-users improving, thus, servive performance and latency. Next generation wireless networks use edge located cache as well as core located cache in order to reduce traffic between the gateway and the internet and make the response of mobile network faster. This is referred to as hierarchical/distributed caching. Dimensioning of individual scenario-based traffic models for health care, museum virtual tours and interactive educational use cases in the context of the LiveCity European Research Project is attempted in this work. ON/OFF source models featuring state dependent active and inactive periods are used in order to describe multi-threaded resource transmissions. A group of chain ON/OFF models holds the parameters of the basic services and defines cache requirements. An architecture that associates distributed cache deployed at eMBMS Gateways per service area is proposed in order to reduce backhaul traffic. A simple algorithm that determines the optimal cache size in a mixture of chain ON/OFF modeled services is presented.
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https://hal.inria.fr/hal-01391024
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Ioannis Stephanakis, Ioannis Chochliouros, George Lymperopoulos, Kostas Berberidis. Optimal Video Delivery in Mobile Networks Using a Cache-Accelerated Multi Area eMBMS Architecture. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.13-23, ⟨10.1007/978-3-662-44722-2_2⟩. ⟨hal-01391024⟩

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