Abstract: An example method includes for enhancing video streaming includes inputting a client-side environment parameter to a client-side neural network (NN) implemented in a client device and then training the client-side NN based on the inputted client-side environment parameter. Thereafter, a client-side encoding bit rate is determined based on a result of the training of the client-side NN and then transmitting the determined client-side encoding bit rate as an input to a server-side neural network (NN) implemented in a server device. The method further includes training the server-side NN based on the inputted client-side encoding bit rate and a server-side environment parameter. Then, a server-side encoding bit rate is determined based on a result of the training of the server-side NN and thereafter, an encoding bit rate of an encoder for the video stream is adjusted based on the determination of the server-side encoding bit rate, and the server-side environment parameter.

Abstract: The disclosure relates to a method and system of selectively deploying an application for facilitating quality-of-experience (QoE) in terms of streaming multimedia content in a networking environment comprising a user-equipment (UE) and a networking node provided with a predictive analysis module. The method comprises: capturing parameters pertaining to UE from at least one of a version of the predictive analysis module with respect to the UE, a current processor occupancy within the UE, a power-level within the UE, network conditions pertaining to the access network etc. One or more of the captured parameters and the observed network conditions is analyzed. Based on analysis, inference is drawn for selecting between the predictive analysis model of the UE and of the networking node for thereby enabling a customized streaming of multimedia content at the UE.

Abstract: An example method includes for enhancing video streaming includes inputting a client-side environment parameter to a client-side neural network (NN) implemented in a client device and then training the client-side NN based on the inputted client-side environment parameter. Thereafter, a client-side encoding bit rate is determined based on a result of the training of the client-side NN and then transmitting the determined client-side encoding bit rate as an input to a server-side neural network (NN) implemented in a server device. The method further includes training the server-side NN based on the inputted client-side encoding bit rate and a server-side environment parameter. Then, a server-side encoding bit rate is determined based on a result of the training of the server-side NN and thereafter, an encoding bit rate of an encoder for the video stream is adjusted based on the determination of the server-side encoding bit rate, and the server-side environment parameter.

Abstract: The disclosure relates to a method and system of selectively deploying an application for facilitating quality-of-experience (QoE) in terms of streaming multimedia content in a networking environment comprising a user-equipment (UE) and a networking node provided with a predictive analysis module. The method comprises: capturing parameters pertaining to UE from at least one of a version of the predictive analysis module with respect to the UE, a current processor occupancy within the UE, a power-level within the UE, network conditions pertaining to the access network etc. One or more of the captured parameters and the observed network conditions is analyzed. Based on analysis, inference is drawn for selecting between the predictive analysis model of the UE and of the networking node for thereby enabling a customized streaming of multimedia content at the UE.