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In recent years video traffic has become the dominant application on the Internet with global year-on-year increases in
video-oriented consumer services. Driven by improved bandwidth in both mobile and fixed networks, steadily reducing
hardware costs and the development of new technologies, many existing and new classes of commercial and industrial
video applications are now being upgraded or emerging. Some of the use cases for these applications include areas such
as public and private security monitoring for loss prevention or intruder detection, industrial process monitoring and
critical infrastructure monitoring. The use of video is becoming commonplace in defence, security, commercial,
industrial, educational and health contexts.
Towards optimal performances, the design or optimisation in each of these applications should be context aware and task
oriented with the characteristics of the video stream (frame rate, spatial resolution, bandwidth etc.) chosen to match the
use case requirements. For example, in the security domain, a task-oriented consideration may be that higher resolution
video would be required to identify an intruder than to simply detect his presence. Whilst in the same case, contextual
factors such as the requirement to transmit over a resource-limited wireless link, may impose constraints on the selection
of optimum task-oriented parameters.
This paper presents a novel, conceptually simple and easily implemented method of assessing video quality relative to its
suitability for a particular task and dynamically adapting videos streams during transmission to ensure that the task can
be successfully completed. Firstly we defined two principle classes of tasks: recognition tasks and event detection tasks.
These task classes are further subdivided into a set of task-related profiles, each of which is associated with a set of taskoriented
attributes (minimum spatial resolution, minimum frame rate etc.). For example, in the detection class, profiles
for intruder detection will require different temporal characteristics (frame rate) from those used for detection of high
motion objects such as vehicles or aircrafts. We also define a set of contextual attributes that are associated with each
instance of a running application that include resource constraints imposed by the transmission system employed and the
hardware platforms used as source and destination of the video stream. Empirical results are presented and analysed to
demonstrate the advantages of the proposed schemes.
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