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Waterloo, Canada

A monitoring system includes a video network analyzer processes the packet data to generate network and media session data relating to the distribution of video content via the data distribution network in at least one media session, wherein the network and media session data includes at least one session metric, location data, protocol identification data, video encoding parameter data, and audio encoding parameter data. An analytics database stores the network and media session data for generation of report data.


A device includes a frame data analyzer that generates buffer increment data based on frame data sent from the media server to the media client and further based on acknowledgement data sent from the media client to the media server. A playback data generator generates playback data based on frame data buffer contents and further based on player state data. A frame buffer model generator generates a buffer fullness indicator and the frame data buffer contents, based on the buffer increment data and the playback data. A player state generator generates the player state data, based on the buffer fullness indicator and further based on media client data, media server data and player command data.


Ghadiyaram D.,University of Texas at Austin | Bovik A.C.,University of Texas at Austin | Yeganeh H.,Avvasi | Kordasiewicz R.,Avvasi | Gallant M.,Avvasi
2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 | Year: 2014

We have created a new mobile video database that models distortions caused by network impairments. In particular, we simulate stalling events and startup delays in over-the-top (OTT) mobile streaming videos. We describe the way we simulated diverse stalling events to create a corpus of distorted videos and the human study we conducted to obtain subjective scores. We also analyzed the ratings to understand the impact of several factors that influence the quality of experience (QoE). To the best of our knowledge, ours is the most comprehensive and diverse study on the effects of stalling events on QoE. We are making the database publicly available [1] in order to help advance state-of-the-art research on user-centric mobile network planning and management. © 2014 IEEE. Source


Yeganeh H.,Avvasi | Kordasiewicz R.,Avvasi | Gallant M.,Avvasi | Ghadiyaram D.,University of Texas at Austin | Bovik A.C.,University of Texas at Austin
2014 IEEE International Conference on Image Processing, ICIP 2014 | Year: 2014

The vast majority of today's internet video services are consumed over-the-top (OTT) via reliable streaming (HTTP via TCP), where the primary noticeable delivery-related impairments are startup delay and stalling. In this paper we introduce an objective model called the delivery quality score (DQS) model, to predict user's QoE in the presence of such impairments. We describe a large subjective study that we carried out to tune and validate this model. Our experiments demonstrate that the DQS model correlates highly with the subjective data and that it outperforms other emerging models. © 2014 IEEE. Source

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