Toward Optimal Deployment of Cloud-Assisted Video Distribution Services

The convergence of mobile communications and cloud computing facilitates the cross-layer network design and content-assisted communication. Mobile video broadcasting can benefit from this trend by utilizing joint source-channel coding and strong information correlation in clouds. In this paper, a knowledge-enhanced mobile video broadcasting (KMV-Cast) is proposed. The KMV-Cast is built on a linear video transmission instead of traditional digital video system, and exploits the hierarchical Bayesian model to integrate the correlated information into the video reconstruction at the receiver. The correlated information is distilled to obtain its intrinsic features, and the Bayesian estimation algorithm is used to maximize the video quality. The KMV-Cast system consists of both likelihood broadcasting and prior knowledge broadcasting. The simulation results show that the proposed KMV-Cast scheme outperforms the typical linear video transmission scheme called Softcast, and achieves 8dB more of the peak signal-to-noise ratio (PSNR) gain at low-SNR channels (i.e., -10dB), and 5dB more of PSNR gain at high-SNR channels (i.e., 25dB). Compared to traditional digital video system, the proposed scheme has 7dB more of PSNR gain than JPEG2000+802.11a scheme at 10dB channel SNR.

“…Mobile network is being merged with cloud computing [5][6][7]. Its advanced network architecture enables mobile users to make full use of information in clouds [8][9][10][11][12].…”

mentioning

confidence: 99%

Knowledge-Enhanced Mobile Video Broadcasting Framework With Cloud Support

Huang

2017

“…Media Cloud Service Providers pull infrastructure resources from traditional cloud service providers, and at the same time, offer metered media services to end-users [6], [19]. Specifically, they can dynamically instantiate virtualized caching and transcoding resources, and decide how to allocate them on each edge server.…”

Section: A System Architecturementioning

confidence: 99%

Optimal Transcoding and Caching for Adaptive Streaming in Media Cloud: an Analytical Approach

Jin

Wen

Westphal

2015

Self Cite

Nowadays, large-scale video distribution feeds a significant fraction of the global Internet traffic. However, existing content delivery networks may not be cost efficient enough to distribute adaptive video streaming, mainly due to the lack of orchestration on storage, computing and bandwidth resources. In this paper, we leverage the media cloud to deliver ondemand adaptive video streaming services, where those resources can be dynamically scheduled in an on-demand fashion. Our objective is to minimize the total operational cost by optimally orchestrating multiple resources. Specifically, we formulate an optimization problem, by examining a three-way trade-off between the caching, transcoding, and bandwidth cost, at each edge server. Then, we adopt a two-step approach to analytically derive the closed-form solution of the optimal transcoding configuration and caching space allocation, respectively, for every edge server. Finally, we verify our solution throughout extensive simulations. The results indicate that, our approach achieves significant cost savings compared with existing methods used in content delivery networks. In addition, we also find the optimal strategy and its benefits can be affected by a list of system parameters, including the unit cost of different resource, the hop distance to the origin server, the Zipf parameter of users' request patterns, and the settings of different bitrate version for one segment.

“…In contrast, DC is so flexible that it can deal with various applications, but at the cost of increasing service cost. That is because the most critical application determines the level of the server [8] (the details please see the following example). As a result, a natural and fundamental question arises: How the cloud designer chooses SC or DC?…”

Section: Introductionmentioning

confidence: 99%

Specific Versus Diverse Computing in Media Cloud

Zhou

2015

Specific computing (SC) and diverse computing (DC), as two main visual computation manners, have been widely utilized in the media cloud. However, how to choose SC or DC in a practical scenario is still an open and challenging problem. Unfortunately, traditional fluid-based analysis method can not address this issue due to the uncertain relationship between the computing manner and service dynamics. In this work, we analytically study the characteristics of SC and DC by designing a so-called collapsing approximation (CA) method to precisely approximate the distribution of the service dynamics. On the qualitative end, we derive an exact expression for the dynamics of CA, thus enabling the cloud designer to choose different computing manners according to the application requests and analyze its impact regarding to the degrees of DC. On the technical end, we show that the evolution of the service dynamics process can be approximated by the unique solution to a collapsing model over a finite time period. The highlight of this paper lies in demonstrating that the optimal computing configuration should largely depend on SC, and a little on DC.