Collaborative hierarchical caching in cloud radio access networks

Li, Xiuhua; Wu, Peiran; Wang, Xiaofei; Li, Keqiu; Han, Zhu; Leung, Victor C. M.

doi:10.1109/infcomw.2017.8116420

Cited by 8 publications

(12 citation statements)

References 14 publications

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“…Authors in [13] proposed the edge computing assisted system for DASH video streaming with the objective of jointly maximizing the QoE of the clients, fair bitrate allocation and balancing the utilized resources among multiple base stations. Along with MEC, the video content caching and retrieval at the network edge within the radio access network (RAN) has been shown to be a promising solution to alleviate significantly the traffic burden on the backhaul network [6], [14], [15], [16]. However, the bitrate adaptation and edge caching solutions in these works do not take into account the optimization of QoE jointly with the traffic on the backhaul network.…”

Section: Related Workmentioning

confidence: 99%

“…If there is no such aforementioned bitrate available, the objective function is evaluated for those set of suitable bitrates which satisfy only the switching threshold compromising the fairness threshold (lines [15][16][17][18][19]. Similarly, the candidate bitrate which maximizes the objective function (9) is chosen as the bitrate for the current chunk of the client.…”

Section: B Self-tuned Bitrate Selectionmentioning

confidence: 99%

“…Otherwise, if the client is about to download the new chunk, the algorithm first assigns the client to the nearest edge server from where the client achieves the highest downlink SNR from the associated base station. The subroutine self-tuned bitrate selection is then called which allocates the most suitable bitrate at which the client downloads the new chunk of video (lines 15,16). Finally, if the client either leaves or finishes its streaming session at the current time slot, its utility (objective value (9)) and the corresponding backhaul traffic are added to the overall system outputs (lines 17-19).…”

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confidence: 99%

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Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Mehrabi

Siekkinen

Ylä-Jääski

2019

IEEE Trans. on Green Commun. Netw.

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Collaborative caching and processing at the network edges through mobile edge computing (MEC) helps to improve the quality of experience (QoE) of mobile clients and alleviate significant traffic on backhaul network. Due to the challenges posed by current grid powered MEC systems, the integration of time-varying renewable energy into the MEC known as green MEC (GMEC) is a viable emerging solution. In this paper, we investigate the enabling of GMEC on joint optimization of QoE of the mobile clients and backhaul traffic in particularly dynamic adaptive video streaming over HTTP (DASH) scenarios. Due to intractability, we design a greedy-based algorithm with self-tuning parameterization mechanism to solve the formulated problem. Simulation results reveal that GMEC-enabled DASH system indeed helps not only to decrease grid power consumption but also significantly reduce backhaul traffic and improve average video bitrate of the clients. We also find out a threshold on the capacity of energy storage of edge servers after which the average video bitrate and backhaul traffic reaches a stable point. Our results can be used as some guidelines for mobile network operators (MNOs) to judge the effectiveness of GMEC for adaptive video streaming in next generation of mobile networks. Index Terms-Green mobile edge computing (GMEC), DASH, Quality of experience (QoE), Fairness, Greedy-based algorithm. Antti Ylä-Jääski received the Ph.D. degree from ETH Zurich in 1993. From 1994 to 2009, he was with Nokia in several research and research management positions, with a focus on future Internet, mobile networks, applications, services, and service architectures. Since 2004, he has been a tenured Professor with the Department of Computer Science, Aalto University. His current research interests include mobile cloud computing, mobile multimedia systems, pervasive computing and communications, indoor positioning and navigation, energy efficient communications and computing, and Internet of Things.

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Section: Related Workmentioning

confidence: 99%

Section: B Self-tuned Bitrate Selectionmentioning

confidence: 99%

mentioning

confidence: 99%

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Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Mehrabi

Siekkinen

Ylä-Jääski

2019

IEEE Trans. on Green Commun. Netw.

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“…Caching contents at the edges, e.g., base stations (BSs) of mobile networks has recently attracted much attention as an effective approach for offloading network traffic while satisfying QoS, by bringing contents closer to users and then satisfying their content requests locally [1], [6]. There have been a great number of studies focusing on the design of content caching schemes in mobile networks.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the concept of Caching-as-a-Service (CaaS) was proposed in [14], focusing on the framework design of virtual caching for offloading network traffic in Cloud-based RANs (C-RANs). Moreover, cooperative BS caching frameworks were proposed in [1], [6], [15], [16], in order to facilitate offloading network traffic by bringing contents closer to users, and improving users' QoS by reducing the time delay of accessing contents. However, most of these studies only focus on content placement, and do not explore the last mile of content delivery via wireless transmissions from BSs to users.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation for cache-enabled cloud-based small cell networks

Wang

Sheng

et al. 2018

Computer Communications

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Edge Caching for Cache Intensity under Probabilistic Delay Constraint

Sanguanpuak

Guruacharya

Rajatheva

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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In order to reduce the latency of data delivery, one of techniques is to cache the popular contents at the base stations (BSs) i.e. edge caching. However, the technique of caching at edge can only reduce the backhaul delay, other techniques such as BS densification will also need to be considered to reduce the fronthaul delay. In this work, we study the trade-offs between BS densification and cache size under delay constraint at a typical user (UE). For this, we use the downlink SINR coverage probability and throughput obtained based on stochastic geometrical analysis. The network deployment of BS and cache storage is introduced as a minimization problem of the product of the BS intensity and cache size which we refer to the product of "cache intensity" under probabilistic delay constraint. We examine the cases when (i) either BS intensity or the cache size is held fixed, and (ii) when both BS intensity and the cache size are vary. For the case when both BS intensity and the cache size are variable, the problem become nonconvex and we convert into a geometric programing which we solve it analytically.

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Collaborative hierarchical caching in cloud radio access networks

Cited by 8 publications

References 14 publications

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Energy-Aware QoE and Backhaul Traffic Optimization in Green Edge Adaptive Mobile Video Streaming

Resource allocation for cache-enabled cloud-based small cell networks

Edge Caching for Cache Intensity under Probabilistic Delay Constraint

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