Cloud Mobile Media: Reflections and Outlook

Wen, Yonggang; Zhu, Xiaoqing; Rodrigues, Joel J. P. C.; Chen, Chang Wen

doi:10.1109/tmm.2014.2315596

Cited by 141 publications

(66 citation statements)

References 91 publications

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“…There is much complementary work concerning video delivery from the cloud [55] and/or with proxy support [56]. Future work could concern exploiting proxy support and/or cloud-computing capabilities in delivery of multi-video stream bundles.…”

Section: Discussionmentioning

confidence: 99%

Optimized Adaptive Streaming of Multi-video Stream Bundles

Carlsson

Eager

Krishnamoorthi

et al. 2017

IEEE Trans. Multimedia

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Abstract-In contrast to traditional video, multi-view video streaming allows viewers to interactively switch among multiple perspectives provided by different cameras. One approach to achieving such a service is to encode the video from all of the cameras into a single stream, but this has the disadvantage that only a portion of the received video data will be used, namely that required for the selected view at each point in time. In this paper we introduce the concept of a "multi-video stream bundle" that consists of multiple parallel video streams that are synchronized in time, each providing the video from a different camera capturing the same event or movie. For delivery we leverage the adaptive features and time-based chunking of HTTPbased Adaptive Streaming (HAS), but now employing adaptation in both content and rate. Users are able to change their viewpoint on-demand and the client player adapts the rate at which data is retrieved from each stream based on the user's current view, the probabilities of switching to other views, and the user's current bandwidth conditions. A crucial component of such a system is the prefetching policy. For this we present an optimization model as well as a simpler heuristic that can balance the playback quality and the probability of playback interruptions. After analytically and numerically characterizing the optimal solution, we present a prototype implementation and sample results. Our prefetching and buffer management solution is shown to provide close to seamless playback switching when there is sufficient bandwidth to prefetch the parallel streams.

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Section: Discussionmentioning

confidence: 99%

Optimized Adaptive Streaming of Multi-video Stream Bundles

Carlsson

Eager

Krishnamoorthi

et al. 2017

IEEE Trans. Multimedia

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“…These new networks are classified as Cloud Mobile Media (CMM) [3]. Nevertheless for these applications in CMM new challenges appear, related to the Quality of Experience (QoE) management [8].…”

Section: Related Workmentioning

confidence: 99%

Using Factor Analysis Techniques to Find Out Objective Video Quality Metrics for Live Video Streaming over Cloud Mobile Media Services

García-Pineda¹,

Felici-Castell²,

Segura-García³

2016

NPA

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The increased adoption of smartphones, the access to mobile broadband networks and the availability of public Clouds allow new multimedia services, called Cloud Mobile Media Services. Under this new architecture the proliferation of live video streaming applications and the Quality of Experience (QoE) given by the final user are an issue, due to the higher and variable delay, as result of the virtualization methods used in the Clouds. Thus in this paradigm new challenges appear related to keep and estimate a good QoE in terms of a standardized subjective video quality called Mean Opinion Score (MOS). In this paper, we propose several objective video quality metrics to estimate the subjective MOS based on Factor Analysis techniques both using Full Reference and Non Reference approaches. We compare the performance of the estimated MOS against publicly available video quality algorithms.

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“…In this condition, the content producer can focus on providing the information object of the video content. Nowadays, adaptive bitrate streaming has been widely adopted to deliver the video content in different bitrates and resolutions to adopt varying network conditions and heterogeneous devices [2]. Video transcoding is used to pre-transcode the original contents into multiple representations.…”

Section: Introductionmentioning

confidence: 99%

“…Second, video transcoding takes excessive time and the content producers need to wait for a long time for content publishing, which is intolerable for the contents needing timely delivery. The emergence of cloud computing technology introduces an opportunity to improve video transcoding [2]. By leveraging the cloud infrastructure, it can transcode video contents more efficiently compared with the traditional transcoding methods.…”

Section: Introductionmentioning

confidence: 99%

Resource provisioning and profit maximization for transcoding in Information Centric Networking

Gao

Wen

Westphal

2016

2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

Self Cite

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Abstract-Adaptive bitrate streaming (ABR) has been widely adopted to support video streaming services over heterogeneous devices and varying network conditions. With ABR, each video content is transcoded into multiple representations in different bitrates and resolutions. However, video transcoding is computing intensive, which requires the transcoding service providers to deploy a large number of servers for transcoding the video contents published by the content producers. As such, a natural question for the transcoding service provider is how to provision the computing resource for transcoding the video contents while maximizing service profit. To address this problem, we design a cloud video transcoding system by taking the advantage of cloud computing technology to elastically allocate computing resource. We propose a method for jointly considering the task scheduling and resource provisioning problem in two timescales, and formulate the service profit maximization as a two-timescale stochastic optimization problem. We derive some approximate policies for the task scheduling and resource provisioning. Based on our proposed methods, we implement our open source cloud video transcoding system Morph and evaluate its performance in a real environment. The experiment results demonstrate that our proposed method can reduce the resource consumption and achieve a higher profit compared with the baseline schemes.

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Cloud Mobile Media: Reflections and Outlook

Cited by 141 publications

References 91 publications

Optimized Adaptive Streaming of Multi-video Stream Bundles

Optimized Adaptive Streaming of Multi-video Stream Bundles

Using Factor Analysis Techniques to Find Out Objective Video Quality Metrics for Live Video Streaming over Cloud Mobile Media Services

Resource provisioning and profit maximization for transcoding in Information Centric Networking

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