Layer approach for HTTP-based low-delay adaptive streaming in mobile networks

The increasing number of mobile devices with high processing power and high-resolution screens had led to an enormous growth of mobile video traffic. Mobile network operators face the requirement to efficiently support large numbers of concurrent unicast streaming sessions. In the present work, the long-term quality of experience perceived by the user, the fairness, and the overall system efficiency are addressed simultaneously from the cross-layer perspective by jointly optimizing the video adaptation and the wireless resource allocation. One fundamental challenge of the cross layer design is that the time scale of video adaptation - seconds - differs by several orders of magnitude from the one of resource allocation - milliseconds. We focus on the low-delay live streaming, which is particularly sensitive to the throughput fluctuation. We consider the streaming both in the downlink and in the uplink, explicitly taking into account the imperfect synchronization in the uplink. Our proposed solution consists of two components. First, we formulate the problem of video adaptation as a quality of experience based max-min optimization problem that leverages the link rate estimate in the lower network layers. Second, we propose a dynamic resource allocation scheme that takes into account the demands of the streaming clients. These two components together aim at a fair and efficient cross-layer streaming system. An accurate estimation of link rate on the time scale of seconds, required for this problem, is particularly difficult in mobile networks. As a separate contribution, several link rate estimation approaches are evaluated. The prediction algorithms assuming the static resource allocation, although computationally less complex, may lead to inaccurate prediction results in comparison to the one when dynamic resource allocation scheme is used. In this work, the spectral efficiency gain by dynamic resource allocation can be approximated and used to improve throughput predictions. We evaluate the proposed approach against state-of-the-art baselines. The results reveal significant improvements of quality of experience in all studied use cases.