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Carsten Griwodz

Researcher at University of Oslo

Publications -  238
Citations -  5263

Carsten Griwodz is an academic researcher from University of Oslo. The author has contributed to research in topics: The Internet & Video quality. The author has an hindex of 32, co-authored 230 publications receiving 4366 citations. Previous affiliations of Carsten Griwodz include Simula Research Laboratory & IBM.

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Proceedings ArticleDOI

Towards middleware services for mobile ad-hoc network applications

TL;DR: This work proposes to develop middleware services that additionally provide services for information sharing in mobile ad-hoc networks, because the possibility to share information is mission critical for many mobile ad -hoc network applications.
Proceedings ArticleDOI

Fine-grained scalable streaming from coarse-grained videos

TL;DR: The far-from-obvious conclusion is reached that layer switching is viable way for bit-rate savings and fine-grained bit- rate adaptation even for rather short times between layer switches.
Proceedings ArticleDOI

An analysis of the heterogeneity and IP packet reordering over multiple wireless networks

TL;DR: With the increasing deployment of wireless technologies, it is often the case that simultaneous coverage of several access networks is available to a single user device, and if all available network interfaces are exploited at the same time, advantages like the aggregation of bandwidth and increased fault tolerance are obtained.
Journal ArticleDOI

Delay-Sensitive Video Computing in the Cloud: A Survey

TL;DR: The recent advances on delay-sensitive video computations in the cloud, which are crucial to cloud-assisted conversational video services, such as cloud gaming, Virtual Reality (VR), Augmented Reality (AR), and telepresence are surveyed.
Proceedings ArticleDOI

EIR — Efficient computer aided diagnosis framework for gastrointestinal endoscopies

TL;DR: A multimedia system to tackle automatic analysis of videos from the human gastrointestinal (GI) tract that combines filters using machine learning, image recognition and extraction of global and local image features, built in a modular way, so that it can be extended.