Live 360° Video Streaming to Heterogeneous Clients in 5G Networks

Abstract

We investigate rate-distortion-computing optimized live 360° video streaming to heterogeneous mobile VR clients in 5G networks. The client population comprises devices that feature single (LTE) or dual (LTE/NR) cellular connectivity. The content is compressed using scalable 360° tiling at the origin and sent towards the clients over a single backbone network link. A mobile edge server then adapts the incoming streaming data to the individual clients and their respective down-link transmission rates using formal rate-distortion-computing optimization. Single connectivity clients are served by the edge server a baseline representation/layer of the content adapted to their down-link transmission capacity and device computing capability. A dual connectivity client is served in parallel a baseline content layer on its LTE connectivity and a complementary viewport-specific enhancement layer on its NR connectivity, synergistically adapted to the respective down-links' transmission capacities and its computing capability. We formulate two optimization problems to conduct the operation of the edge server in each case, taking into account the key system components of the delivery process and induced end-to-end latency, aiming to maximize the immersion fidelity delivered to each client. We explore respective geometric programming optimization strategies that compute the optimal solutions at lower complexity. We rigorously analyze the computational complexity of the two optimization algorithms we formulate. In our evaluation, we demonstrate considerable performance gains over multiple assessment factors relative to two state-of-the-art techniques. We also examine the robustness of our approach to inaccurate user navigation prediction, transient NR link loss, dynamic LTE bandwidth variations, and diverse 360° video content. Finally, we contrast our results over five popular video quality metrics. The paper makes a community contribution by publicly sharing a dataset that captures the rate-quality trade-offs of the 360° video content used in our evaluation, for multiple contemporary quality metrics, to stimulate further studies and follow up work.