Abstract: There is provided a method for estimating play out resolution of a video delivered to a client device by an encrypted video stream communicated over a network. The method selects a current chunk of the encrypted video stream comprising data packets expected to carry video data of the same level of playout resolution and determines values for a predetermined set of features indicative of conditions in the network. By accessing a pregenerated model, a corresponding set of state transition probabilities is obtained, defining a Markov chain whose states comprise the different levels of resolution. The determined state transition probabilities are then used to calculate, from a first probability distribution arising from a first or previous step in the Markov chain, a second probability distribution for the plurality of states of the Markov chain expected to result from the indicated network conditions.

Abstract: We generally describe a method for analyzing data traffic quality of data traffic flowing between a user equipment and a server of a video service provider. The data traffic includes video traffic and audio traffic. The method includes separating, in the data traffic, the video traffic from the audio traffic, and analyzing, based on the separated video traffic, the data traffic quality of the data traffic flowing between the user equipment and the server of the video service provider.

Abstract: There is provided a method for estimating play out resolution of a video delivered to a client device by an encrypted video stream communicated over a network. The method selects a current chunk of the encrypted video stream comprising data packets expected to carry video data of the same level of playout resolution and determines values for a predetermined set of features indicative of conditions in the network. By accessing a pregenerated model, a corresponding set of state transition probabilities is obtained, defining a Markov chain whose states comprise the different levels of resolution. The determined state transition probabilities are then used to calculate, from a first probability distribution arising from a first or previous step in the Markov chain, a second probability distribution for the plurality of states of the Markov chain expected to result from the indicated network conditions.