Content Acquisition Processing, Monitoring, and Forensics for AT&T Services (CONSENT)

What is CONSENT?

Given the high volumes of video content and various delivery channels (internet streaming, content on-demand, IP television, etc.) a system is needed to automatically verify that each piece of content is delivered with the highest quality. CONSENT fills this gap by providing a platform for detection and analysis of content for security and quality assurance. CONSENT was created to analyze and detect video quality errors through a number of means.

  • Detect video anomalies due to content and network problems
  • Network behavior (loss, jitter, join leave rates, etc.)
  • Content protocol and standards conformance (syntax, structure, etc.)
  • Media-based content verification (EPG/media mismatch, content structure, Ads correctness, metadata, activity on channels, etc.)

Helping customers and network operators stop errors

An extensive set of control procedures have been created in CONSENT to help correlate errors that a customer reports to video-broadcast events and to pre-emptively identify and diagnose errors that occur within the network, before these errors are passed onto the customer. All content and events are logged at various stages in the CONSENT architecture, so a deep forensic problem analysis is never more than a few clicks away.

CONSENT also pinpoints content problems not only within a provider's network but also in generic Internet video, making it a good solution for anyone trying to provide a consistent, high quality video consumption experience. The illustration below depicts the overall CONSENT system diagram and its many options for acquisition, consumption, and diagnosis.

CONSENT Overview Diagram

Error Simulations

Even the most heavily tested systems will experience errors if they are only tested with lab conditions. Incorporating experience from in-field technicians, network managers, and engineers that define next-generation standards, a vast array of error simulations were included in the CONSENT architecture. Thus, a content delivery network can be stress-tested for conditions that may not be possible in real-world deployments and simultaneously teach CONSENT how to predict quality errors by using its correlation engine that observes and learns from prior events.

Project Members

David Gibbon

Behzad Shahraray

Related Projects

Project Space

AT&T Application Resource Optimizer (ARO) - For energy-efficient apps

Assistive Technology

CHI Scan (Computer Human Interaction Scan)

CoCITe – Coordinating Changes in Text

Connecting Your World



E4SS - ECharts for SIP Servlets

Scalable Ad Hoc Wireless Geocast

AT&T 3D Lab

Graphviz System for Network Visualization

Information Visualization Research - Prototypes and Systems

Swift - Visualization of Communication Services at Scale

Smart Grid

Speech Mashup

Omni Channel Analytics

Speech translation

StratoSIP: SIP at a Very High Level


Content Augmenting Media (CAM)

Content-Based Copy Detection

Content Analytics - distill content into visual and statistical representations

MIRACLE and the Content Analysis Engine (CAE)

Social TV - View and Contribute to Public Opinions about Your Content Live

Visual API - Visual Intelligence for your Applications

Enhanced Indexing and Representation with Vision-Based Biometrics

Visual Semantics for Intuitive Mid-Level Representations

eClips - Personalized Content Clip Retrieval and Delivery

iMIRACLE - Content Retrieval on Mobile Devices with Speech

AT&T WATSON (SM) Speech Technologies

Wireless Demand Forecasting, Network Capacity Analysis, and Performance Optimization