VSR-UAiC: An Upload Adaptive Bitrate Framework in Video Super-Resolution-Enabled Crowdsourced Live Streaming

Recently, the popularity of crowdsourced live streaming (CLS) has increased significantly. To overcome the bandwidth limitations of the broadcaster’s stream in the first mile, some research has introduced video super-resolution (VSR) algorithms at the source server. However, this implementation brings an additional computational burden. In VSR-enabled CLS, the stream push bitrate of the broadcaster will directly determine the volume of data transmitted in the first mile and processed by the source server. An inappropriate upload bitrate can lead to unacceptable delays and resource wastage. To address this issue, this paper proposes a VSR-UAiC framework, which implements an upload adaptive bitrate (ABR) control in VSR-enabled CLS. The VSR-UAiC framework is capable of perceiving multi-dimensional information, including network conditions, computing power, video content, and viewer requests. VSR-UAiC employs an end-to-end reinforcement learning (RL) algorithm to train agents for obtaining the optimal upload ABR strategy. A series of experiments have demonstrated the superiority of the VSR-UAiC framework over alternative solutions in terms of latency, cost, and system capacity.