Joint online transcoding and geo-distributed delivery for dynamic adaptive streaming

Wang, Zhi; Sun, Lifeng; Wu, Chuan; Zhu, Wenwu; Yang, Shiqiang

doi:10.1109/infocom.2014.6847928

Cited by 46 publications

(36 citation statements)

References 13 publications

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“…Jokhio et al [26] studied the basic dynamic allocation and release of VMs and the decision making on whether performing transcoding tasks in advance so as to avoid excess storage on cloud servers. There are some recent relevant works including [28], [29] and [30] which proposed to adaptively transcode the videos according to cost or user preferences. The videos with high storage cost or low user preference can be processed with a transcode-on-request manner which only transcodes the videos when there are requests for them.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

QoS-Aware Resource Allocation for Video Transcoding in Clouds

Wei

Cai

Foh

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-As the "biggest big data", video data streaming in the network contributes the largest portion of global traffic nowadays and in future. Due to heterogeneous mobile devices, networks and user preferences, the demands of transcoding source videos into different versions have been increased significantly. However, video transcoding is a time-consuming task and how to guarantee quality-of-service (QoS) for large video data is very challenging, particularly for those real-time applications which hold strict delay requirement such as live TV. In this paper, we propose a cloud-based online video transcoding system (COVT) aiming to offer economical and QoS guaranteed solution for online large-volume video transcoding. COVT utilizes performance profiling technique to obtain the different performance of transcoding tasks in different infrastructures. Based on the profiles, we model the cloud-based transcoding system as a queue and derive the QoS values of the system based on queuing theory. With the analytically derived relationship between QoS values and the number of CPU cores required for transcoding workloads, COVT is able to solve the optimization problem and obtain the minimum resource reservation for specific QoS constraints. A task scheduling algorithm is further developed to dynamically adjust the resource reservation and schedule the tasks so as to guarantee the QoS in runtime. We implement a prototype system of COVT and experimentally study the performance on real-world workloads. Experimental results show that COVT effectively provisions minimum number of resources for predefined QoS. To validate the effectiveness of our proposed method under large scale video data, we further perform simulation evaluation which again shows that COVT is capable to achieve economical and QoS-aware video transcoding in cloud environment.

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Section: Related Workmentioning

confidence: 99%

“…Our effort is on how to request minimum cloud computing resource while still meeting the QoS requirements of video transcoding. Our work can be complement to [28], [29], [30], by outsourcing their online transcoding part from local servers to the cloud.…”

Section: Related Workmentioning

confidence: 99%

QoS-Aware Resource Allocation for Video Transcoding in Clouds

Wei

Cai

Foh

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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“…Zhi et al [23] propose to leverage underused CDN computing resources to jointly transcode and deliver videos by having CDN servers transcode and store the most popular video segments.…”

Section: Related Workmentioning

confidence: 99%

Transcoding live adaptive video streams at a massive scale in the cloud

Aparicio-Pardo

Pires

Blanc

et al. 2015

Proceedings of the 6th ACM Multimedia Systems Conference

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More and more users are watching online videos produced by non-professional sources (e.g., gamers, teachers of online courses, witnesses of public events) by using an increasingly diverse set of devices to access the videos (e.g., smartphones, tablets, HDTV). Live streaming service providers can combine adaptive streaming technologies and cloud computing to satisfy this demand. In this paper, we study the problem of preparing live video streams for delivery using cloud computing infrastructure, e.g., how many representations to use and the corresponding parameters (resolution and bit-rate). We present an integer linear program (ILP) to maximize the average user quality of experience (QoE) and a heuristic algorithm that can scale to large number of videos and users.We also introduce two new datasets: one characterizing a popular live streaming provider (Twitch) and another characterizing the computing resources needed to transcode a video. They are used to set up realistic test scenarios. We compare the performance of the optimal ILP solution with current industry standards, showing that the latter are sub-optimal. The solution of the ILP also shows the importance of the type of video on the optimal streaming preparation. By taking advantage of this, the proposed heuristic can efficiently satisfy a time varying demand with an almost constant amount of computing resources.

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“…However, this may introduce significant storage cost. Moreover, since only a small range of segments are requested 2 System architecture of the mobile media cloud by many users [26], we only cache the most popular ones.…”

Section: System Architecturementioning

confidence: 99%

“…Intuitively, one solution is to pre-transcode each video content into multiple versions with a variety of resolutions. However, this way obviousely leads to significant waste on storage and computing resource because a substantial fraction of video contents are seldom being viewed at all [26]. Therefore, a partial transcoding scheme for content distribution in a media cloud is studied in [10].…”

Section: Introductionmentioning

confidence: 99%

Improving Energy Efficiency for Mobile Media Cloud via Virtual Machine Consolidation

et al. 2015

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Mobile media applications are on the rise due to the explosive popularity of mobile devices. Advances in mobile media cloud (MMC) make it a promising solution to serve those huge multimedia applications. In MMC, virtualization is adopted to allocate resources elastically from a shared resource pool. Therefore, effective virtual machine (VM) consolidation is of paramount importance to maximize energy efficiency. In this paper, we consider a scenario that mobile media cloud performs video streaming and transcoding for viewers with different mobile devices. We formulate the VM consolidation problem as a mixed integer linear programming. Under this framework, the minimum energy consumption and the number of physical machines (PMs) in operation are derived. Based on these analytical results, for homogeneous media cloud, we develop an approximation algorithm for VM consolidation and placement which jointly considers CPU and bandwidth constraints. For heterogeneous media cloud, we derive an upper and a lower bound of the number of PMs and their energy consumption. Trace-driven simulations demonstrate that our proposed algorithm significantly reduces energy consumption and the number of PMs used.

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Joint online transcoding and geo-distributed delivery for dynamic adaptive streaming

Cited by 46 publications

References 13 publications

QoS-Aware Resource Allocation for Video Transcoding in Clouds

QoS-Aware Resource Allocation for Video Transcoding in Clouds

Transcoding live adaptive video streams at a massive scale in the cloud

Improving Energy Efficiency for Mobile Media Cloud via Virtual Machine Consolidation

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