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Minimizing server throughput for low-delay live streaming in content delivery networks

Published:07 June 2012Publication History

ABSTRACT

Large-scale live streaming systems can experience bottlenecks within the infrastructure of the underlying Content Delivery Network. In particular, the "equipment bottleneck" occurs when the fan-out of a machine does not enable the concurrent transmission of a stream to multiple other equipments. In this paper, we aim to deliver a live stream to a set of destination nodes with minimum throughput at the source and limited increase of the streaming delay. We leverage on rateless codes and cooperation among destination nodes. With rateless codes, a node is able to decode a video block of k information symbols after receiving slightly more than k encoded symbols. To deliver the encoded symbols, we use multiple trees where inner nodes forward all received symbols. Our goal is to build a diffusion forest that minimizes the transmission rate at the source while guaranteeing on-time delivery and reliability at the nodes. When the network is assumed to be lossless and the constraint on delivery delay is relaxed, we give an algorithm that computes a diffusion forest resulting in the minimum source transmission rate. We also propose an effective heuristic algorithm for the general case where packet loss occurs and the delivery delay is bounded. Simulation results for realistic settings show that with our solution the source requires only slightly more than the video bit rate to reliably feed all nodes.

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            cover image ACM Conferences
            NOSSDAV '12: Proceedings of the 22nd international workshop on Network and Operating System Support for Digital Audio and Video
            June 2012
            116 pages
            ISBN:9781450314305
            DOI:10.1145/2229087

            Copyright © 2012 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 7 June 2012

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            Overall Acceptance Rate118of363submissions,33%

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