Given the constantly growing demand for live streaming services, live transcoding has become compulsory and very challenging. So far, investigations have been confined to satisfy a huge number of users for ensuring the Quality of Experience (QoE). The aim of this paper is to propose a framework architecture following ESTI-NFV (Network Function Virtualization) model, whereby the transcoding and streaming Virtual Network Functions (VNFs) would be running on top of multiple cloud domains. By respecting ESTI-NFV model, we ensure the flexibility of our virtual delivery platform that scales up/down and in/out relative to the changing demands of the end-users in order to reduce cost. For this purpose, this paper presents a new framework for managing the virtual live transcoding and streaming VNFs on top of multiple cloud domains for ensuring the QoE while reducing the cost. In order to develop such a framework, we have done a set of experimental benchmarking of transcoding and streaming VNFs using variant flavors (i.e., in terms of CPU and Memory resources). The obtained results will be explored later for developing an intelligent algorithm that will be integrated with the proposed framework in managing different transcoding and streaming VNFs in an efficient manner.

/pdf/efficient-transcoding-and-streaming-mechanism-in-multiple-91qmpgn16y.pdf

Efficient Transcoding and Streaming Mechanism in Multiple Cloud Domains

Abstract In recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user. 

Cloud media video encoding: review and challenges

Nowadays, the advent of social networks have revolutionised the traditional communication media. In recent years, the number of social media providers has rapidly grown. In this context, one of the major problems is the on-demand video streaming provisioning. In fact, more and more users require to post and access in real-time videos from anywhere in a short time. Therefore, a denial of service condition can cause for social media providers a loss of users and a consequent lose of money. Commonly, videos, before to be delivered, must be transcoded in order to fits both users’ hardware/software device and network capabilities, raising a big video data processing issue. In order to address such a concern, in this paper, we propose a Cloud federation system that enables social media providers to work together so as to take the advantages of a scalable video processing service. Experimental results demonstrate how the overhead due to setup and maintenance tasks of the federated environment is negligible compared to the benefits in terms of video transcoding performance. Moreover, we also demonstrate how Cloud federation can lighten and speed up the whole video processing service, by introducing an additional parallelization level.

A big video data transcoding service for social media over federated clouds

Video transcoding at the edge: cost and feasibility perspective

The coexistence of parallel applications in shared computing nodes, each one featuring different Quality of Service (QoS) requirements, carries out new challenges to improve resource occupation while keeping acceptable rates in terms of QoS. As more application-specific and system-wide metrics are included as QoS dimensions, or under situations in which resource-usage limits are strict, building and serving the most appropriate set of actions (application control knobs and system resource assignment) to concurrent applications in an automatic and optimal fashion become mandatory. In this paper, we propose strategies to build and serve this type of knowledge to concurrent applications by leveraging Reinforcement Learning techniques. Taking multi-user video transcoding as a driving example, our experimental results reveal an excellent adaptation of resource and knob management to heterogeneous QoS requests, and increases in the amount of concurrently served users up to 1.24 × compared with alternative approaches considering homogeneous QoS requests.

Leveraging knowledge-as-a-service (KaaS) for QoS-aware resource management in multi-user video transcoding

High-definition video applications are often challenging for mobile devices due to their limited processing capability and bandwidth-constrained network connection. Video transcoding has become an inevitable technology for on-demand video streaming service which needs to be done on the go in real-time for mobile devices. Since video transcoding involves extensive computation, performing transcoding using cloud resource is more cost friendly and time effective. It is challenging to use the cloud resources for video transcoding that minimises the operating cost. In this paper, we propose a dynamic resource provisioning algorithm for allocating virtual machine to scale video transcoding services on a given IaaS cloud and conducted experiments in CloudSim and compared the proposed system with conventional system. The experimental results show that the proposed system outperforms the conventional systems.

Dynamic Resource Provisioning for Video Transcoding in IaaS Cloud

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Dynamic Resource Provisioning for Video Transcoding in IaaS Cloud