@article{Jia2026, 
author = {Chenge Jia and Rongqing Liu and Zhiqiang Li and Jie Zheng and Jie Ren},
title = {Perceptually-Driven Video Super Resolution for Mobile Live Streaming: An Adaptive Cloud-Assisted Approach},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {2},
pages = {957-975},
keywords = {mobile live streaming, video super-resolution (VSR), user perceptual quality, adaptive model selection, VSR task scheduling},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010132},
doi = {10.26599/TST.2024.9010132},
abstract = {The increasing demand for high-definition live video streaming services on mobile devices is often hindered by unstable network conditions and limited computational capabilities. To address these issues, we introduce an adaptive video super-resolution (VSR) based mobile live streaming method, MOBLIVE. The core idea of MOBLIVE is to selectively offload regions of video frames that critically influence user perception quality to the server-side VSR model for enhancement. To further improve the video quality, we deploy a predictive model to choose the optimal VSR model for each selected region. Additionally, we employ an adaptive graphics processing unit (GPU) scheduling strategy that optimizes the allocation of multiple VSR tasks across multiple GPUs. Experimental results show that our approach outperforms the state-of-the-art method in video multi-method assessment fusion (VMAF) and reduces the latency by an average of 73.7% in the typical networking environment.}
}