Cloud media video encoding:review and challenges

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

Toward a General Parametric Model for Assessing the Impact of Video Transcoding on Objective Video Quality

Video transcoding can cause degradation to an original video. Currently, there is no general model that assesses the impact of video transcoding on video quality. Such a model could play a critical role in evaluating the quality of the transcoded video, and thereby optimizing delivery of video to end-users while meeting their expectations. The main contribution of this research is the development and substantiation of a general parametric model, called the Video Transcoding Objective-quality Model (VTOM), that provides an extensible video transcoding service selection mechanism, which takes into account both the format and characteristics of the original video and the desired output, i.e., viewing format with preferred quality of service. VTOM represents a mathematical function that uses a set of media-related parameters for the original video and desired output, including codec, bit rate, frame rate, and frame size to predict the quality of the transcoded video generated from a specific transcoding. VTOM includes four quality sub-models, each describing the impact of each of these parameters on objective video quality, as well as a weighted-product aggregation function that combines these quality sub-models with four additional error sub-models in a single function for assessing the overall video quality. I compared the predicted quality results generated from the VTOM with quality values generated from an existing objective-quality metric. These comparisons yielded results that showed good correlations, with low error values. VTOM helps the researchers and developers of video delivery systems and applications to calculate the degradation that video transcoding can cause on the fly, rather than evaluate it statistically using statistical methods that only consider the desired output. Because VTOM takes into account the quality of the input video, i.e., video format and characteristics, and the desired quality of the output video, it can be used for dynamic video transcoding service selection and composition. A number of quality metrics were examined and used in development of VTOM and its assessment. However, this research discovered that, to date, there are no suitable metrics in the literature for comparing two videos with different frame rates. Therefore, this dissertation defines a new metric, called Frame Rate Metric (FRM) as part of its contributions. FRM can use any frame-based quality metric for comparing frames from both videos. Finally, this research presents and adapts four Quality of Service (QoS)-aware video transcoding service selection algorithms. The experimental results showed that these four algorithms achieved good results in terms of time complexity, success ratio, and user satisfaction rate

Algorithms and methods for video transcoding.

Video transcoding is the process of dynamic video adaptation. Dynamic video adaptation can be defined as the process of converting video from one format to another, changing the bit rate, frame rate or resolution of the encoded video, which is mainly necessitated by the end user requirements. H.264 has been the predominantly used video compression standard for the last 15 years. HEVC (High Efficiency Video Coding) is the latest video compression standard finalised in 2013, which is an improvement over H.264 video compression standard. HEVC performs significantly better than H.264 in terms of the Rate-Distortion performance. As H.264 has been widely used in the last decade, a large amount of video content exists in H.264 format. There is a need to convert H.264 video content to HEVC format to achieve better Rate-Distortion performance and to support legacy video formats on newer devices. However, the computational complexity of HEVC encoder is 2-10 times higher than that of H.264 encoder. This makes it necessary to develop low complexity video transcoding algorithms to transcode from H.264 to HEVC format. This research work proposes low complexity algorithms for H.264 to HEVC video transcoding. The proposed algorithms reduce the computational complexity of H.264 to HEVC video transcoding significantly, with negligible loss in Rate-Distortion performance. This work proposes three different video transcoding algorithms. The MV-based mode merge algorithm uses the block mode and MV variances to estimate the split/non-split decision as part of the HEVC block prediction process. The conditional probability-based mode mapping algorithm models HEVC blocks of sizes 16×16 and lower as a function of H.264 block modes, H.264 and HEVC Quantisation Parameters (QP). The motion-compensated MB residual-based mode mapping algorithm makes the split/non-split decision based on content-adaptive classification models. With a combination of the proposed set of algorithms, the computational complexity of the HEVC encoder is reduced by around 60%, with negligible loss in Rate-Distortion performance, outperforming existing state-of-art algorithms by 20-25% in terms of computational complexity. The proposed algorithms can be used in computation-constrained video transcoding applications, to support video format conversion in smart devices, migration of large-scale H.264 video content from host servers to HEVC, cloud computing-based transcoding applications, and also to support high quality videos over bandwidth-constrained networks

Optimising Networks For Ultra-High Definition Video

The increase in real-time ultra-high definition video services is a challenging issue for current network infrastructures. The high bitrate traffic generated by ultra-high definition content reduces the effectiveness of current live video distribution systems. Transcoders and application layer multicasting (ALM) can reduce traffic in a video delivery system, but they are limited due to the static nature of their implementations. To overcome the restrictions of current static video delivery systems, an OpenFlow based migration system is proposed. This system enables an almost seamless migration of a transcoder or ALM node, while delivering real-time ultra-high definition content. Further to this, a novel heuristic algorithm is presented to optimise control of the migration events and destination. The combination of the migration system and heuristic algorithm provides an improved video delivery system, capable of migrating resources during operation with minimal disruption to clients. With the rise in popularity of consumer based live streaming, it is necessary to develop and improve architectures that can support these new types of applications. Current architectures introduce a large delay to video streams, which presents issues for certain applications. In order to overcome this, an improved infrastructure for delivering real-time streams is also presented. The proposed system uses OpenFlow within a content delivery network (CDN) architecture, in order to improve several aspects of current CDNs. Aside from the reduction in stream delay, other improvements include switch level multicasting to reduce duplicate traffic and smart load balancing for server resources. Furthermore, a novel max-flow algorithm is also presented. This algorithm aims to optimise traffic within a system such as the proposed OpenFlow CDN, with the focus on distributing traffic across the network, in order to reduce the probability of blocking

Feature-based generation of pervasive systems architectures utilizing software product line concepts

As the need for pervasive systems tends to increase and to dominate the computing discipline, software engineering approaches must evolve at a similar pace to facilitate the construction of such systems in an efficient manner. In this thesis, we provide a vision of a framework that will help in the construction of software product lines for pervasive systems by devising an approach to automatically generate architectures for this domain. Using this framework, designers of pervasive systems will be able to select a set of desired system features, and the framework will automatically generate architectures that support the presence of these features. Our approach will not compromise the quality of the architecture especially as we have verified that by comparing the generated architectures to those manually designed by human architects. As an initial step, and in order to determine the most commonly required features that comprise the widely most known pervasive systems, we surveyed more than fifty existing architectures for pervasive systems in various domains. We captured the most essential features along with the commonalities and variabilities between them. The features were categorized according to the domain and the environment that they target. Those categories are: General pervasive systems, domain-specific, privacy, bridging, fault-tolerance and context-awareness. We coupled the identified features with well-designed components, and connected the components based on the initial features selected by a system designer to generate an architecture. We evaluated our generated architectures against architectures designed by human architects. When metrics such as coupling, cohesion, complexity, reusability, adaptability, modularity, modifiability, packing density, and average interaction density were used to test our framework, our generated architectures were found comparable, if not better than the human generated architectures

Adapting mobile systems using logical mobility primitives

Mobile computing devices, such as personal digital assistants and mobile phones, are becoming increasingly popular, smaller, more capable and even fashionable personal items. Combined with the recent advent of wireless networking techniques, users are equipped with mobile devices of significant computational abilities, which are able to wirelessly access information by dynamically connecting to many different networks. Despite the ubiquity of mobile devices, mobile systems are built using monolithic architectures, use a small set of predefined interaction paradigms and do not exploit or adapt to the dynamicity of their local or remote context. Applications deployed on mobile devices face considerable challenges posed by their changing surroundings. One of the main peculiarities of mobile devices is heterogeneity, which may occur in software, hardware and network protocols. Mobile systems may carry a large number of different applications, use different operating systems and middleware and, often, have more than one network interface. A further challenge is their considerable variation in the computational resources available, such as battery power, CPU speed, network bandwidth and volatile and persistent memory. Moreover, mobile computing systems are highly dynamic systems, in terms of their surroundings, implying that the requirements for applications deployed on a mobile device are a moving target. Changes in the requirements (such as integration with a new service) may require changes to the application. Consequently, these changes may mean that the application behaviour needs to adapt. This thesis argues that the potential of the ubiquity of mobile devices cannot be realised using static and monolithic architectures, as mobile systems need to be able to adapt to accommodate changes to their environment. It investigates the use of three technologies to offer adaptation to mobile devices: Logical mobility techniques, component systems and middleware technologies. More specifically, this thesis presents the SATIN (System Adaptation Targeting Integrated Networks) component metamodel, a lightweight local component metamodel that offers the flexible use of logical mobility primitives. The metamodel is instantiated to build the SATIN middleware system, a component-based mobile computing middleware that uses the mobility primitives exported by the metamodel to reconfigure itself and applications running on top of it. The suitability of SATIN for the creation of adaptable mobile systems is demonstrated, by using it to implement and evaluate a number of applications showing different aspects of adaptation. Moreover, existing projects are reengineered to run as SATIN components, showing the flexibility of the approach and the advantages gained over the originals

Recent Advances in Wireless Communications and Networks

This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters

Modeling And Dynamic Resource Allocation For High Definition And Mobile Video Streams

Video streaming traffic has been surging in the last few years, which has resulted in an increase of its Internet traffic share on a daily basis. The importance of video streaming management has been emphasized with the advent of High Definition: HD) video streaming, as it requires by its nature more network resources. In this dissertation, we provide a better support for managing HD video traffic over both wireless and wired networks through several contributions. We present a simple, general and accurate video source model: Simplified Seasonal ARIMA Model: SAM). SAM is capable of capturing the statistical characteristics of video traces with less than 5% difference from their calculated optimal models. SAM is shown to be capable of modeling video traces encoded with MPEG-4 Part2, MPEG-4 Part10, and Scalable Video Codec: SVC) standards, using various encoding settings. We also provide a large and publicly-available collection of HD video traces along with their analyses results. These analyses include a full statistical analysis of HD videos, in addition to modeling, factor and cluster analyses. These results show that by using SAM, we can achieve up to 50% improvement in video traffic prediction accuracy. In addition, we developed several video tools, including an HD video traffic generator based on our model. Finally, to improve HD video streaming resource management, we present a SAM-based delay-guaranteed dynamic resource allocation: DRA) scheme that can provide up to 32.4% improvement in bandwidth utilization

A Cloud Platform-as-a-Service for Multimedia Conferencing Service Provisioning

Multimedia Conferencing is the real-time exchange of media content (e.g. voice, video and text) between multiple participants. It is the basis of a wide range of conferencing applications such as massively multi-player online games and distance learning applications. For faster development as well as cost efficiency, developers of such conferencing applications can use conferencing services (e.g. dial-in audio conference) provided by third-parties. However, the third-party service providers face several challenges with respect to conferencing service provisioning (i.e. service development, deployment and management). One challenge is mastering complex low-level details of conferencing technologies, protocols and their interactions. Another challenge is resource elasticity. Number of conference participants varies during runtime. So resource utilization in an elastic manner is a critical factor to achieve cost efficiency. Cloud Computing can help tackle these challenges. It is a paradigm for swiftly provisioning a shared pool of configurable resources (e.g. services, applications, network and storage) on demand. It has three main service models: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). Using a PaaS, service providers can provision conferencing services easily and offer them as SaaS. Nonetheless, cloud-based provisioning of conferencing services still remains a big challenge due to the shortcomings of existing PaaS. In this thesis, a PaaS architecture for conferencing service provisioning is proposed. It is based on a business model from the state of the art. It relies on conferencing IaaSs that, instead of VMs, offer conferencing substrates (e.g. dial-in signaling, video mixer and audio mixer). The conferencing PaaS enables composition of new conferences from substrates on the fly. Moreover, it provides conferencing service providers, who are experienced in programming, with high-level interfaces to abstract the internal complexities of conferencing. In order for PaaS to scale ongoing conferences elastically, an algorithm is also presented in this thesis. The conferencing PaaS is prototyped and performance measurements are made. The proposed algorithm’s performance is also evaluated