<p>To address the issues of playback stalling and low video quality in video streaming for IoT-centric applications under bandwidth-constrained environments, a multi-parameter collaborative sensing-based adaptive bitrate control algorithm is proposed to enhance the quality of experience. First, a multi-parameter collaborative bandwidth prediction model is constructed by integrating various parameters, including download rate of video segment, bandwidth fluctuation magnitude and real-time buffer occupancy, to enable comprehensive sensing of the network environment. Then, the current buffer state is collaboratively integrated with the bandwidth change to enable dynamic estimation of available bandwidth. Furthermore, a buffer-oriented multi-parameter switching values model is constructed based on the predicted bandwidth, video segment duration and the available bitrate levels. This model collaboratively incorporates multiple parameters and introduces a joint cost function aimed at improving the average bitrate of video streaming, which is used to optimize the buffer occupancy switching values and enable adaptive bitrate control under varying network conditions. According to experimental results, the bitrate switching frequency is reduced by approximately 29.4% and the average bitrate is increased by about 11.3% under bandwidth-constrained environments, resulting in a significant improvement in overall quality of experience.</p>