The Collaboration for Content Delivery and Network Infrastructures: A Survey

Jia, Qingmin; Xie, Renchao; Huang, Tao; Liu, Jiang; Liu, Yunjie

doi:10.1109/access.2017.2715824

Cited by 34 publications

(14 citation statements)

References 104 publications

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“…content pushed to them and pulling content on their own [37]. Guo et al [38] formulated mixed consumption behavior of consumers as push and pull systems, to provide optimal push frequency for content provider.…”

Section: B Content Delivery Systemmentioning

confidence: 99%

Cost Optimization for the Coupled Video Delivery Networks

Chen

Zhang

et al. 2019

IEEE Access

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Video delivery networks (VDNs) are one of the key enablers for alleviating contradiction between demands increasing for online video and response time of users accessing video content. However, existed works mainly adopt either proactive or reactive delivery scheme, which leads to the problems of backbone traffic overloaded and delays increased. In these works, the impact of performance indicators, such as bandwidth, delay, and personalized demand on delivery cost are not comprehensively taken into account. To overcome these problems, we propose a cost optimization for coupled video delivery model (CO-CVDM), which combines coupled proactive delivery and coupled reactive delivery method to minimize delivery cost in terms of bandwidth consumption, delay performance, personalized demand and construct a multicast delivery tree for the video delivery. The coupled delivery cost minimization problem under constraint conditions is formulated. Since this problem is NP-hard and is prohibitively difficult to solve, we develop a heuristic multicast delivery tree (HMDT) algorithm to provide more optimized approximate cost. Based on this, we extend CO-CVDM to distribute video in a dynamic scenario. Specifically, with dynamic change of user's requirement for video, the process of video delivery is truncated into a sequence of static graphs and the change of interest attenuation is modeled with human forgetting curve. Finally, the numerical simulations are conducted to validate the advantage of CO-CVDM in terms of optimization delivery cost, and the delivery cost is inversely proportional to the attenuation of user interest. INDEX TERMS Coupled video delivery, cost optimization, bandwidth, delay, personalized demand.

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Section: B Content Delivery Systemmentioning

confidence: 99%

Cost Optimization for the Coupled Video Delivery Networks

Chen

Zhang

et al. 2019

IEEE Access

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“…There are still emerging opportunistic techniques that improve content caching, based on an information theory perspective. For example, in [8], [13], [14], the authors proposed the use of mature information coding schemes in content caching strategies that can improve network performance and meet the traffic demand.…”

Section: A Background and Related Workmentioning

confidence: 99%

Dynamic Programming Based Content Placement Strategy for 5G and Beyond Cellular Networks

Ayenew

Xenakis

Passas

et al. 2018

2018 IEEE 23rd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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Optimal caching strategies of popular contents in heterogeneous cellular networks are studied. The increasing demand for data traffic by users of the wireless network can be handled by rapaciously caching most frequently accessed contents by users. Hence, we propose an efficient popular content placement strategy, the first step in the content caching process, typically for popular video files. To do so, we introduce a novel approach for caching popular contents. This caching strategy follows a dynamic programming approach to tackle the optimization complexity of selecting most popular files among a wide range of files, under certain constraints. The proposed strategy gives the combination of popular files to be cached that maximizes the optimal cache hit probability with a pseudo-polynomial time complexity. To that end, we used the well-known resourcing algorithm, called the 0/1-Knapsack problem, assuming that files are cached without partitioning.

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“…Thanks to the development of cloud computing technology [2–4], offloading computation tasks to cloud computing server for processing first gets the attention from the academia and industry [5–7]. However, the computation offloading to the cloud computing server usually brings a serious weakness on network delay.…”

Section: Introductionmentioning

confidence: 99%