With the rapid growth of the Internet and smart cities, video data has become a primary contributor to total Internet traffic, making video transmission technology essential in modern information systems. However, large-scale video uploads to the cloud are hindered by dynamic network conditions and data redundancy, limiting transmission performance. This paper proposes an adaptive multi-stream transmission method with bandwidth awareness (MSBA) for End-Cloud systems. MSBA introduces feature-compressed streams and semantic video streams, categorizing network environments by available bandwidth and fluctuation levels to adaptively transmit appropriate data streams. This approach enables the cloud to efficiently process video data under various network conditions by maximizing bandwidth utilization. Experimental results demonstrate that MSBA effectively maintains visual focus area quality while achieving high compression rates, reducing video transmission delay by 57.22% to 84.98% com-pared to baseline methods. Overall, our solution can effectively reduce the video transmission delay while maintaining the SSIM of the focus area in the video.