Energy-Efficient Multi-User Adaptive 360$^{\circ }$ Video Streaming: A Two-Step Approach With Device Video Super-Resolution

In this paper, we propose a device energy-efficient two-step adaptive scheme for tile-based 360$^\circ$ video streaming to support enhanced multi-user viewing quality-of-experience (QoE) in a time-slotted system. Specifically, each video chunk is first prefetched based on the predictive field-of-view (FoV), and the FoV quality of the chunk is then enhanced at a closer-to-playback time instant based on the updated FoV prediction with improved accuracy. Both transmission-driven and device video super-resolution (VSR)-driven methods are adaptively selected to enable efficient video chunk enhancement. At each time slot, the incremental QoE gain of each user is characterized via a time-difference approach, based on which the best candidate chunk is determined. Then, a single-slot problem is formulated for maximizing the total incremental QoE gain while minimizing the total device energy consumption. A particle swarm optimization (PSO)-based iterative solution is proposed to obtain optimal bandwidth allocation, bitrate level selection, and enhancement method selection for multiple users. Extensive simulation results demonstrate that our proposed solution outperforms benchmark schemes in terms of average viewing QoE, average device energy consumption, and average utility.