Towards Cost-Efficient Video Transcoding in Media Cloud: Insights Learned From User Viewing Patterns

Gao, Guanyu; Zhang, Weiwen; Wen, Yonggang; Wang, Zhi; Zhu, Wenwu

doi:10.1109/tmm.2015.2438713

Cited by 84 publications

(32 citation statements)

References 19 publications

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“…Jin et al [20] apply caching to adaptive streaming, and study the optimal transcoding and caching allocation scheme in media cloud in order to minimize the total operational cost of delivering on-demand adaptive video streaming, with the assumption that each mobile user accesses one edge server for video downloading. 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.…”

Section: Related Workmentioning

confidence: 99%

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Toni

Zou

et al. 2018

IEEE Trans. Multimedia

134

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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

134

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“…Equation (10) ensures that there is no indirect connection to any VM which has no direct connection. In addition, equations (11) and (12) consider all possible indirect connections from a participant‫ݑ‬ to the VM ‫ݒ‬ through all other VMs. Based on these connections, equation (13) ensures that the final video streams come from the VMs which are directly or indirectly connected to all participants.…”

Section: ) Constraintsmentioning

confidence: 99%

Resource Allocation Mechanism for Media Handling Services in Cloud Multimedia Conferencing

Soltanian

Naboulsi

Glitho

et al. 2019

IEEE J. Select. Areas Commun.

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Multimedia conferencing is the conversational exchange of multimedia content between multiple parties. It has a wide range of applications (e.g., Massively Multiplayer Online Games (MMOGs) and distance learning). Media handling services (e.g., video mixing, transcoding, and compressing) are critical to multimedia conferencing. However, efficient resource usage and scalability still remain important challenges. Unfortunately, the cloud-based approaches proposed so far have several deficiencies in terms of efficiency in resource usage and scaling, while meeting Quality of Service (QoS) requirements. This paper proposes a solution which optimizes resource allocation and scales in terms of the number of participants while guaranteeing QoS. Moreover, our solution composes different media handling services to support the participants' demands. We formulate the resource allocation problem mathematically as an Integer Linear Programming (ILP) problem and design a heuristic for it. We evaluate our proposed solution for different numbers of participants and different participants' geographical distributions. Simulation results show that our resource allocation mechanism can compose the media handling services and allocate the required resources in an optimal manner while honoring the QoS in terms of end-to-end delay.

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“…Transcoding also causes certain extra cost. Reference [15] pointed out this problem and proposed an online partial transcoding caching scheme used in cloud computing network with an aim to minimize the total extra cost caused by the transcoding and storage. Reference [16] in 2018 proposed a cloud-based architecture to allocate transcoding tasks among virtual machines in a cache system to decrease the cost of streaming service provider and pointed out transcoding based video caching is worth being further studied.…”

Section: Related Workmentioning

confidence: 99%

Transcoding Based Video Caching Systems: Model and Algorithm

Zhao

et al. 2018

Wireless Communications and Mobile Computing

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The explosive demand of online video watching brings huge bandwidth pressure to cellular networks. Efficient video caching is critical for providing high-quality streaming Video-on-Demand (VoD) services to satisfy the rapid increasing demands of online video watching from mobile users. Traditional caching algorithms typically treat individual video files separately and they tend to keep the most popular video files in cache. However, in reality, one video typically corresponds to multiple different files (versions) with different sizes and also different video resolutions. Thus, caching of such files for one video leads to a lot of redundancy since one version of a video can be utilized to produce other versions of the video by using certain video coding techniques. Recently, fog computing pushes computing power to edge of network to reduce distance between service provider and users. In this paper, we take advantage of fog computing and deploy cache system at network edge. Specifically, we study transcoding based video caching in cellular networks where cache servers are deployed at the edge of cellular network for providing improved quality of online VoD services to mobile users. By using transcoding, a cached video can be used to convert to different low-quality versions of the video as needed by different users in real time. We first formulate the transcoding based caching problem as integer linear programming problem. Then we propose a Transcoding based Caching Algorithm (TCA), which iteratively finds the placement leading to the maximal delay gain among all possible choices. We deduce the computational complexity of TCA. Simulation results demonstrate that TCA significantly outperforms traditional greedy caching algorithm with a decrease of up to 40% in terms of average delivery delay.

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Towards Cost-Efficient Video Transcoding in Media Cloud: Insights Learned From User Viewing Patterns

Cited by 84 publications

References 19 publications

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

Resource Allocation Mechanism for Media Handling Services in Cloud Multimedia Conferencing

Transcoding Based Video Caching Systems: Model and Algorithm

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