The key ideas to keep in mind about building an effective system that exploits streaming data are the following: Real-time analysis of streaming data can empower you to react to events and insights as they happen. There have been profound changes in the ways of multimedia acquisition, distribution and consumption. Waze is just one straightforward example of the time-value of information: the value of that particular knowledge decreases quickly with elapsed time. In Chapter 14 “A chunkless peer-to-peer transport protocol for multimedia streaming”, the authors describe a P2P transport protocol suited for multimedia streaming. But these after-the-fact insights do little to help the morning commuter get to work faster. An effective message-passing technology decouples the sources and consumers, which is a key to agility. Industrial settings provide examples from the IoT where streaming data is of value in a variety of ways. The short answer to that question is that superb new technologies are now available to handle streaming data at high-performance levels and at large scale—and that is leading more organizations to handle data as a stream. Stream Analytics is better in real-time data handle scenarios. Manufacturers may sell services that draw insights from this data or in some cases sell or license access to the data itself. The latter three points (persistence of streaming data, geo-distributed replication, and the central importance of the correct messaging layer) are relatively new aspects of the preferred design for streaming architectures. In this way, engineers are able to take action in a timely manner. An Huawei Cloud architecture diagram is a visualization of your cloud-based solution that uses Huawei Cloud. Using real-time streaming input from millions of drivers, Waze reports current traffic and road information. The new technologies we discuss in this book can deliver rates of millions of messages per second, even while persisting the messages. It is challenging to maintain the quality-of-service (QoS) of streaming applications over bandwidth constrained, error prone and highly dynamic networks. The following diagram presents the live streaming video architecture you can automatically deploy using the solution's implementation guide and accompanying AWS CloudFormation template. Highly effective messaging technologies collect streaming data from many sources—sometimes hundreds, thousands, or even millions—and deliver it to multiple consumers of the data, including but not limited to real-time applications. HTTP Streaming Architecture. Additionally, these streams of smart-part reports are also a monetizable product themselves. Use Azure Stream Analytics to do a preliminary and necessary data processing and conversion and output the data to azure data lake and use data lake analytics to further process the data. One of the foremost sources of continuous data is from sensors in the Internet of Things (IoT), and a rapidly evolving sector in IoT is the development of futuristic “connected vehicles.”. This chapter concludes with a discussion providing an overall picture of the developments in mesh coding and the directions for future research. A streaming solution is a platform that hosts your live stream. Each cluster contains 6 ALUs, several local register files, and executes completely static VLIW instructions. It’s a bold statement, but today's users demand instant access to data and applications that operate with extreme accuracy. Consider what happens when sensor data is examined along with long-term detailed maintenance histories for parts used in pumps or other industrial equipment. Vehicle connectivity for Earth-bound cars has come a long way since the Apollo missions. Chapter 15 with the title “Low latency audio streaming for internet-based musical interaction” covers the technical issues related to the subject of playing live music with musicians distributed over the Internet. In Chapter 17 “Perspectives of the application of video streaming to education”, the author analyses the application of video-streaming in video-lectures. Sync all your devices and never lose your place. The described protocol is characterized by the robustness to data losses and the low-start times. The emergence of P2P network leads to an architecture shift from client-server streaming to P2P streaming. The design blueprint helps you express design and deployment ideas of your Huawei Cloud infrastructure thoroughly. The upshot of these changes is that getting real-time insights from streaming data has gone from promise to widespread practice. As a result, the research on wireless streaming has drawn tremendous attentions by both academia and industry. In the past decade, we have witnessed the great success of streaming media in network broadcasting, distant learning, digital library, and video on demand (VOD) among others. Note. Perhaps the customer would be encouraged by a discount coupon, particularly if it were for an item or service that really appealed to them. Receive Free Worldwide Shipping on Orders over US$ 295, Streaming Media Architectures, Techniques, and Applications: Recent Advances, Advances in Multimedia and Interactive Technologies, InfoSci-Social Sciences Knowledge Solutions – Books, Content delivery network for streaming media, Design and deployment of practical streaming media systems, Digital right management for streaming media, Future trends and promising directions in streaming media, Indexing, organization and retrieval for streaming media applications, Novel architectures and protocols for streaming media, Peer-to-Peer architecture for streaming media. Streaming media is one of the most exciting and active research topics with continuing significant progress in the multimedia area, where streaming media has been experiencing dramatic growth and stepped into mainstream media communications. Figure 1-4 is a diagram showing several different ways that a stream of test results data might be used. Overview. Applications should use the Sample Grabber filter or implement a custom filter to get data from a DirectShow filter graph. During the past decade, the media communication system has evolved from the conventional desktop computing and wired communication to mobile computing and wireless communication. The benefits of handling streaming data well are not limited to getting in-the-moment actionable insights, but that is one of the most widely recognized goals. In our healthcare example, there are multiple data sources coming from medical tests such as EKGs, blood panels, or MRI machines that feed in a stream of test results. Latency and audio/video being out of sync. Instead, organizations benefit by adopting this streaming approach as an essential aspect of efficient, overall architecture. Moreover, the limited and changing bandwidth of peers further complicate the problem of QoS control. What kinds of communications can be helpful for it? What all this means is that streaming data is an essential part of the success of the IoT at many levels. With the right technologies, it’s possible to replicate streaming data to geo-distributed data centers. Use Azure Stream Analytics to do a I suggest you combine these 2 ways together. The technological stack is a set of all software and hardware capabilities that ensure the functioning of any technology. One of the reasons modern systems can now more easily handle streaming data is improvements in the way message-passing systems work. Google Cloud Solutions Architecture Reference Infrastructure Modernization. If you use Amazon Web Services, you will love our online AWS Architecture diagram tool. This is not a problem if the messaging software has the needed capabilities to support a durable, replayable record. One of the interesting aspects of this example is that we may want the data stream to serve as a durable, auditable record of the test results for several purposes, such as an insurance audit (labeled as use type “C” in the figure). O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers. This chapter first surveys the typical algorithms in static and dynamic 3D meshes coding,where the coding and streaming of gigantic 3D models are specially introduced. The target audience of this book would be those interested in various aspects of streaming media, such as coding, transmission, architecture and applications. Streaming media is inherently a cross-disciplinary subject that involves information theory, signal processing, communication and networking etc. But for many use cases, batch does not sufficiently reflect the way life happens. In our healthcare example, there are multiple data sources coming from medical tests such as EKGs, blood panels, or MRI machines that feed in a stream of test results. Chapter 1 entitled “Scalable video coding: techniques and applications for adaptive streaming” covers the topic of making use of scalable video content in streaming frameworks and applications. There are many potential advantages of handling data as streams, but until recently this method was somewhat difficult to do well. Moreover, some studies of SVC over WLAN using cross-layer design techniques are presented. Chapter 16 entitled “The 3rd Generation Partnership Project Packet-Switched Streaming (3GPP-PSS): fundamentals and applications” embodies the chronological advances of 3GPP-PSS, whose specifications define the framework for streaming capabilities for 3GPP mobile devices and networks. Audio Mixer, Graphics, Media, etc. Coding and transmission definitely lie in the core position in streaming media, and these research topics have been extremely active in recent years. In addition, each implementation choice is a compromise between the industrial feasibility and the adaptation efficiency. These included overall direction and distance back to the Lunar Module that would carry them home. An effective message-passing system is much more than a queue for a real-time application: it is the heart of an effective design for an overall big data architecture. In computing, batch processing is a good way to deal with huge amounts of distributed data, and batch-based computational approaches such as MapReduce or Spark are still useful in many situations. Huge volumes of sensor data for a wide variety of parameters are collected and stored, mainly intended to be used in case of an accident or malfunction. This chapter shows how streaming can take place in those challenging environments. Even analysis of sporadic events such as website traffic can benefit from a streaming data approach. Surprisingly, among the most requested services that automobile drivers want from their connectivity is to listen to their own music playlist or to more easily use their cell phone while they are driving—it’s almost as though they want a cell phone on wheels. After more than a decade of development, substantial advances have been achieved in the diverse areas of streaming media, and a number of promising research directions are springing up. Streaming media is one of the most exciting and active research topics with continuing significant progress in the multimedia area, where streaming media has been experiencing dramatic growth and stepped into mainstream media communications. They are said to form a peer-to-peer network of nodes. As a result, the end user can enjoy on-the-fly representation of multimedia content without downloading the entire file beforehand. Apart from the entertainment industry, wireless streaming can also find extensive applications in military and disaster rescue where wireless sensors are widely deployed. A live streaming solution allows you to capture video in real-time and broadcast it to consumers in real time, such as streaming interviews, conferences, and sporting events online. P2P streaming has become the most powerful and popular solution for large-scale streaming services due to its decentralization, self-organization, and flexibility. More advanced resource reciprocation strategies, where peers make foresighted decisions on their resource distribution in a way that maximizes their cumulative utilities are also discussed. The authors compare the respective advantages and disadvantages of these two coding techniques. Live Coding 12-Factor App. Stream-based computing is becoming the norm for data-driven organizations. These stream processing analytics technologies are extremely important, and there are some excellent new tools now available, as we discuss in Chapter 2. In this solution, video is captured by a video camera and sent to a Live Event input endpoint. The possibilities are intriguing. An old way of thinking about streaming data is “use it and lose it.” This approach assumed you would have an application for real-time analytics, such as a way to process information from the stream to create updates to a real-time dashboard, and then just discard the data. Persistence of data streams has real benefits. We will explain why this broader view of streaming architecture is valuable, but first we take a look at how people use streaming data, now or in the very near future. The event stream for the sensor data now acts as a “time machine” that lets you look back, with the help of machine learning models, to find anomalous patterns in measurement values prior to a failure. (As of the date of writing this book, modern connected cars do not communicate with anyone on the moon, although they readily make use of 4G networks.). Coding and transmission definitely lie in the core position in streaming media, and these research topics have been extremely active in recent years. These actions might include the transactions involving a credit card or the sequence of events related to logins for a banking website. Peer-to-peer (P2P) computing or networking is a distributed application architecture that partitions tasks or workloads between peers. Chapter 12 with the title “A survey of P2P data-driven live streaming systems” presents a survey of “data-driven” or “unstructured” peer-to-peer live media streaming techniques, which have been introduced in the recent years to overcome the limitation of classic application-layer tree overlays. The solution’s AWS CloudFormation template launches the AWS products and services necessary to ingest, transcode, and deliver live streaming video. Hi all, I'm working on building some architecture for a live streaming environment. Conceptually, HTTP Live Streaming consists of three parts: the server … I suggest you combine these 2 ways together. Kappa Architecture. The authors also describe choices one can make in terms of network architecture and then focus on the input parameters used to evaluate network conditions and to perform the adaptation. Being able to process streaming data via a 4G network and deliver reports to drivers in a timely manner is essential for this navigation tool to work as it is intended. Specifically, the recent standard H.264/SVC, i.e., the scalable extension of the widely used H.264/AVC coding scheme, and its deployment for adaptive streaming, the combined activities of content adaptation and streaming, are considered. Simple is ideal for beginners and producers with smaller workflows (1-2 cameras, few graphics, some video playback). © 2020, O’Reilly Media, Inc. All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. What would a typical server architecture look like? Chapter 2 with the title “Adapting multimedia streaming to changing networkconditions” focuses on the application layer techniques that adapt to the changes in network conditions, including layered encoding, rate shaping, adaptive error control, and smoothing. (based on question comments) My suggestion would be to use a streaming video CDN that supports mobile devices and has edge transcoding. Also any recommendations for a video player? This will display one Output window and a multiview where you will click each source to select what is shown in the Output. As mentioned earlier, the benefits of adopting a streaming style of handling data go far beyond the opportunity to carry out real-time or near–real time analytics, as powerful as those immediate insights may be. The many academic areas covered in this publication include, but are not limited to: The advances in computation and networking, as well as the prevalence of media sharing on the net have significantly increased the availability of multimedia resources. With anomaly detection techniques and very low-latency technologies, cyber attacks by humans or robots may be discovered quickly so that action can be taken to thwart the intrusion or at least to mitigate loss. This facility is depicted in Figure 1-3. Data such as information about train speed, location, and function as well as track conditions are transmitted as continuous streams of data that make it possible for computer applications to provide low-latency insights as events happen. In Chapter 4 “Dirac video codec: introduction”, the authors present the fundamental architecture of the Dirac video encoder that is engineered by British Broadcasting Corporation (BBC) aiming at a wide range of applications from storage of video content to streaming video. But as it turns out, that’s not the only advantage to be gained from using streaming data, as we discuss later in this chapter. AWS provides a real-time subtitling solution for live streaming video content that combines Amazon Transcribe, Amazon Translate, and AWS Lambda to build a serverless architecture that automatically generates multi-language subtitles for your live streaming videos. With the excellent mobility, wireless streaming has turned the on-demand access of rich media content anywhere on any device into a reality. 19 Architecture diagram with a broker Source System 1 Storage systemSource System 2 Source System 3 Ingest Ingest Ingest Extract Streaming engine Push Message broker 20. The first thing to consider for your live streaming studio is the right space. The figure to the right shows a block diagram of the Imagine stream processor. In contrast, even if you carry buckets very quickly to the recipient, the water delivered by bucket (batch) will never occur as a continuous stream. This chapter is intended to inspire additional efforts leading to video routing techniques optimized to different topologies, the physical medium, network channels, and energy constraints. Many web and cloud … Here is a basic diagram for the Kappa architecture that shows two layers system of operation for this data processing architecture. Motivated by its wide-ranging application potential, great efforts have been as well as are being dedicated to the research on steaming media to attack technical challenges as discussed in the following. The most essential requirement of stream processing is one or more sources of data, also known as producers. But with the new design of streaming architecture, multiple consumers might make use of this data right away, in addition to the real-time analytics program. Consider the opportunities for improving customer experience and raising a customer’s tendency to buy something as they pass through a brick-and-mortar store. Chapter 5 with the title “3D mesh model coding” addresses the coding and streaming issues of 3D model. In cases where an upstream queuing system for messages was used, it was perhaps thought of only as a safety buffer to temporarily hold event data as it was ingested, serving as a short-term insurance against an interruption in the analytics application that was using the data stream. This “connected car” could talk to Earth via audio or video transmissions. It turns out that a streaming architecture forms the core for a wide-ranging set of processes, some of which you may not previously have thought of in terms of streaming. About the Multimedia Streaming Architecture. These new approaches to customer-responsive, in-the-moment marketing are already being implemented by some large retail merchants, in some cases developed in-house and in others through vendors who provide innovative new services. With a well-designed project, it is possible to monitor a large variety of things that take place in a system. The proposed approach mixes theoretical results, when available, with empirical observations in order to give the best possible insights. In the older style of working with streaming data, the data might have been single-purpose: read by the real-time application and then discarded. In the past, in order to handle data analysis at scale, data was collected and analyzed in batch. Hotels and other service organizations are also beginning to look at how these approaches can help them better recognize return customers or be alert to constantly changing levels needed for service at check-in or in the hotel lounge. Chapter 8 entitled “Robust video streaming over MANET and VANET” studies the technical issues of video streaming over both Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs). Besides, you can also connect those AWS shapes with traditional UML shapes like nodes, components and artifacts in UML deployment diagram for better representation of ideas. This approach not only saves money; in some cases, it may save lives. Perhaps a discount coupon code shows up on the customer’s phone as they reach the electronics department. The idea was that the data in the event stream no longer had value beyond the real-time analytics or that there was no easy or affordable way to persist it, but that’s changing. As can be seen from the above introductions, this book spans a number of interdependent and emerging topics in streaming media. This book is a comprehensive collection of topics including media coding, wireless/mobile video, P2P media streaming, and applications of streaming media. A fully reconfigurable futuristic Factory 2050 is scheduled to open there in 2016. Chapter 11 entitled “Peer-to-peer networks – protocols, cooperation and competition” discusses P2P systems that have been deployed in file sharing and real-time media streaming. 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