Network assisted rate adaptation for conversational video over LTE, concept and performance evaluation

This work investigates rate adaptation of conversational video in a mobile system using Long Term Evolution, LTE, and where the adaptation is assisted by the radio network. The conventional way to do rate adaptation is through adaptation in the end-points where the transmission rate is selected based on measurements of received packets. This study investigates two network-based algorithms for rate adaptation, a rate fair algorithm that assigns the same rate to all conversational video users in a cell, and a resource fair algorithm that aims to give all users in the cell a fair amount of resources. Both algorithms are combined with delay based scheduling. Both network-based algorithms perform excellently. The delay stays low even when the resource utilization is close to 100%, and the video rate is adapted to the system load. As could be expected, the average user rates are higher with the resource fair algorithm. An end-point based adaptation algorithm is investigated as well, but it cannot keep a low delay at high load.