HbbTV 2 - Deep Dive and Hands-on
This tutorial gives an overview of the Hybrid Broadcast Broadband Television Standard (HbbTV) with particular focus on the latest HbbTV 2 specification.
The first part of the tutorial provides a short introduction to the HbbTV standard highlighted with some practical examples and real-world applications. In the second part, we will present the most relevant features introduced in HbbTV 2 such as the new HTML5 APIs, companion screens and media synchronisation. In contrast to the new HbbTV 2 features, we will discuss new use cases and applications that are enabled by these features. Afterwards, we will present HbbTV-related specifications like Operator Applications and Application Discovery over Broadband. The last part of the tutorial gives an overview on tools for validating and developing HbbTV applications.
Introduction in HbbTV
- What is HbbTV
- How it Works
- History and Evolution of the Versions
- Real World HbbTV Apps
HbbTV 2 Features
- HTML5 associated technologies: CSS3, DOM3, HTML5 Video/Audio, Canvas 2D, Web Sockets, Web Storage, Web Audio
- Subtitles: EBU profile of W3C TTML
- Companion Screen: Discovery, Launch and App2App Communication
- Multi-stream Synchronization, Multi-device Synchronization and Application & content synchronisation
- Updated DVB-DASH: High Dynamic Range Video (HDR), High Frame Rate video (HFR), Next Generation Audio (NGA)
- HEVC Video
- Non-realtime Content Delivery via Broadcast
- Multiple HTML5 Video Elements
- Support of Additional Input Devices (Mouse, Keyboard)
HbbTV 2 Enabled Use Cases
- Advert Insertion into VoD content
- Advert Overlays Synchronized with the Broadcast
- Alternative or Personalized Audio on Companion Screen
- Personal Programme Guide and Additional Information on Companion Screen
- Play-along experience (Quiz show) and Multi-player Games
- Push to Mobile
- 360° Video
Other HbbTV related Specs
- Operator Applications (OpApps)
- Application Discovery over Broadband
- IP-Delivered Broadcast Channels and Related Signalling of HbbTV Applications
- HbbTV Targeted Advertising
- HbbTV DASH DRM Reference Application
- HbbTV/DVB DASH Content Validation Tool
- MPAT: HbbTV Application Toolkit for non-programmers based on WordPress
Lecturer: Louay Bassbouss, Christian Fuhrhop
Deep Media - Media meets AI
When entertainment meets mathematics. Nowadays, the media value chain gets support by various algorithms from the AI domain to help creators, improve distributions, enhance advertising and satisfy consumers. Each new media asset comes with a lot of meta data – e.g., manually produced and automatically generated descriptive data as well as implicit and explicit usage data. For service providers, the analysis of this data becomes more and more crucial. It begins with trend analysis for creatives and sales, comes to processing and preparation for optimized distributions, plays a role in quality assurance, the creation of personalized user experiences and finally supports business analytics.
It is important to understand how to distinguish a set of different concepts and terms in the area of Data Science and Artificial Intelligence. In this tutorial, we will give a brief introduction about differences and commons for: Big Data, Artificial Intelligence, Recommender Systems, Data Mining, Data Science, Machine Learning, Neuronal Networks and Deep Learning – just to mention a few of them. This tutorial gives an overview of the latest techniques and focusses on some recent application fields where media can benefit from AI.
We will present different application areas for Artificial Intelligence in the modern connected world – with a special focus on these applications that were applied to media assets during creation or for distribution and consumption. In hands on sessions we will practically apply these concepts and show algorithms at work while we explain how they work. Among others in the area of Machine Learning supported Per Title Encoding and Intelligent Analysis of Streaming Metrics.
Lecturers: Dr. Christopher Krauß, Sebastian Schmidt, Christoph Müller
Internet Delivered Media - Streaming Tech Update
As part of this tutorial, we will give an overview as well as best practices for playback and creation of adaptive bitrate (ABR) content. With streaming formats such as Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and HTTP Live Streaming (HLS), content providers can reach many devices (mobile, desktop, TV, etc.) over-the-top (OTT). The MPEG Common Media Application Format (CMAF) standard enables interoperability between both streaming formats by leveraging the same media format. HTML5 APIs Media Source Extensions (MSE) and Encrypted Media Extensions (EME) enable playback interoperability across all browser-based platforms.
In order to distribute premium content, Digital Rights Management (DRM) is needed to protect the media streams. A multi-DRM approach is required in order to protect content with more than one DRM system - the MPEG Common Encryption (CENC) standard enables this. In addition, media streaming features such as streaming analytics, client coordination, ad-insertion, low-latency streaming and 360° streaming will be covered in the tutorial. Latest advancements in video encoding allow for title-based encoding, making traditional, generic encoding ladders obsolete and thereby saving CDN costs while boosting the quality of experience.
We will demonstrate the key technologies in multiple live demos and identify challenges and best practices when applying the technologies in production.
Foundations of Adaptive Streaming
- Streaming Formats: DASH, CENC, HLS, CMAF
- Web Media APIs: MSE/EME, fetch(), XHR, WebSocket
- Standards update: DASH-IF, WAVE etc.
- Cross-platform deployment to SmartTVs, HbbTV, FireTV, Chromecast, AppleTV, iOS, Android, Desktop etc.
- Feature support: DRM, Codecs, Casting
- Web, native or hybrid apps?
Hands-On: Web Media Streaming
- Per-Title Encoding
- Analyzing OTT services
- How to use DRM in Web browsers
- Dynamic Ad-insertion
- Streaming Metrics Analytics using SAND
- Low-latency streaming
- 360° Streaming
Lecturers: Stefan Pham, Daniel Silhavy