A Segment-Based Storage and Transcoding Trade-off Strategy for Multi-version VoD Systems in the Cloud

Zhao, Hui; Zheng, Qinghua; Zhang, Weizhan; Du, Biao; Li, Haifei

doi:10.1109/tmm.2016.2612123

Cited by 42 publications

(18 citation statements)

References 36 publications

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“…Gao et al [21] investigate the tradeoff between storage and transcoding computation in the cloud, and propose a cost-efficient partial transcoding scheme for content management based on user viewing patterns. Zhao et al [22] further develop a video segment-based caching strategy for multiple representation VoD systems to minimize the storage and transcoding costs. In order to cope with dynamic requests, the work in [9] proposes an online pre-fetching algorithm to adaptively pre-fetch adaptive streaming video segments while respecting the limited bottleneck bandwidth between the content server and the edge server.…”

Section: Related Workmentioning

confidence: 99%

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Toni

Zou

et al. 2018

IEEE Trans. Multimedia

126

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Caching at mobile edge servers can smooth temporal traffic variability and reduce service load of base stations in mobile video delivery. However, the assignment of multiple video representations to distributed servers is still a challenging question in the context of adaptive streaming, since any two representations from different videos or even from the same video will compete for the limited caching storage. It is therefore important, yet challenging, to optimally select the cached representations for each edge server in order to effectively reduce the service load of base station while maintaining a high quality of experience (QoE) for users. To address this, we study on a QoE-driven mobile edge caching placement optimization problem for dynamic adaptive video streaming that properly takes into account the different ratedistortion (R-D) characteristics of videos and the coordination among distributed edge servers. Then, by the optimal caching placement of representations for multiple videos, we maximize the aggregate average video distortion reduction of all users while minimizing the additional cost of representation downloading from the base station, subject not only to the storage capacity constraints at the edge servers, but also to the transmission and initial startup delay constraints at the users. We formulate the proposed optimization problem as an integer linear program (ILP) to provide the performance upper bound, and as a submodular maximization problem with a set of knapsack constraints to develop a practically feasible cost benefit greedy algorithm. The proposed algorithm has polynomial computational complexity and a theoretical lower bound on its performance. Simulation results further show that the proposed algorithm is able to achieve a near-optimal performance with very low time complexity. Therefore, the proposed optimization framework reveals the caching performance upper bound for general adaptive video streaming systems, while the proposed algorithm provides some design guidelines for the edge servers to select the cached representations in practice based on both the video popularity and content information.

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Section: Related Workmentioning

confidence: 99%

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Toni

Zou

et al. 2018

IEEE Trans. Multimedia

126

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“…At present, the research on the traffic flow shunting mechanism in a heterogeneous network environment mostly determines the optimization target according to the characteristics of the service data, resulting in the transformation of the traffic flow diversion problem into an optimization problem [14,15]. The above optimization target is then used to maximize or minimize processing to obtain an optimal service offload strategy.…”

Section: Related Workmentioning

confidence: 99%

A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks

Ding

et al. 2019

Electronics

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With the development and the characteristics of terminal services of the 5G (5th-Generation network) Internet of Vehicles (IoVs), this paper proposes a distributed splitting strategy for multi-type services of 5G V2X (Vehicle to X) networks. Based on a service-oriented adaptive splitting strategy in heterogeneous networks, combined with various service types such as communications between the networks, terminal, and base stations, and the value-added services of 5G IoVs, the proposed strategy jointly considers delay and cost as optimization goals. By analyzing the characteristics of the different services, the proposed traffic flow splitting strategy is modeled as an optimization problem to efficiently split services in 5G V2X networks. The simulation results show that by setting the traffic distribution policy for each service, the distributed traffic flow splitting strategy can significantly improve network transmission efficiency and reduce the service costs in a vehicle V2X network.

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“…CALMS [13] adaptively leased and adjusted cloud server resources in a granularity to accommodate the temporal and spatial dynamics of demands from live streaming users. The research [15] introduced a segment-based storage and transcoding trade-off strategy for multi-version VoD systems in the cloud. In addition, some media cloud computing studies also examined the load balancing problem among servers for multimedia computing, such as for image enhancement or video transcoding, to efficiently utilize the parallel computing power of the cloud [16, 31].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, the media cloud came into existence [2]. Some researchers have already studied how the media cloud can be efficiently used for cloud-based media streaming applications [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. Because these media streaming applications are resource-intensive, how to maximize resource utilization is still a major challenge for cloud-based media streaming service providers.…”

Section: Introductionmentioning

confidence: 99%

Deploying external bandwidth guaranteed media server clusters for real-time live streaming in media cloud

Zhang

et al. 2019

PLoS ONE

Self Cite

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The cloud-based media streaming service is a promising paradigm for multimedia applications. It is attractive to media streaming service providers, who wish to deploy their media server clusters in a media cloud at reduced cost. Since the real-time live streaming service is both a bandwidth-intensive and quality-sensitive application, how to optimize the internal bandwidth utilization of a data center network (DCN) as well as guarantee the external bandwidth of the real-time live streaming application, is a key issue of deploying virtual machine (VM)-hosted media server cluster in a media cloud. Therefore, in this study, we propose an external-bandwidth-guaranteed media server cluster deployment scheme in media cloud. The approach simultaneously considers the outside bandwidth requirement of a tree-based media server cluster for live streaming and the intra-bandwidth consumption of a DCN. The proposed scheme models the optimal problem as a new terminal-Steiner-tree-like problem and provides an approximate algorithm for placing the media servers. Our evaluation results show that the proposed scheme guarantees the external bandwidth requirement of a real-time live streaming application, at the same time, greatly reduces the intra-bandwidth utilization of a media cloud with different DCN structures.

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A Segment-Based Storage and Transcoding Trade-off Strategy for Multi-version VoD Systems in the Cloud

Cited by 42 publications

References 36 publications

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks

Deploying external bandwidth guaranteed media server clusters for real-time live streaming in media cloud

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