Minimizing Packet Retransmission for Real-Time Video Analytics

In smart-city and video analytics (VA) applications, high-quality data streams (video frames) must be accurately analyzed with a low delay. Since maintaining high accuracy requires compute-intensive deep neural nets (DNNs), these applications often stream massive video data to remote, more powerful cloud servers, giving rise to a strong need for low streaming delay between video sensors and cloud servers while still delivering enough data for accurate DNN inference. In response, many recent efforts have proposed distributed VA systems that aggressively compress/prune video frames deemed less important to DNN inference, with the underlying assumptions being that (1) without increasing available bandwidth, reducing delays means sending fewer bits, and (2) the most important frames can be precisely determined before streaming. This short paper challenges both views. First, in high-bandwidth networks, the delay of real-time videos is primarily bounded by packet losses and delay jitters, so reducing bitrate is not always as effective as reducing packet retransmissions. Second, for many DNNs, the impact of missing a video frame depends not only on itself but also on which other frames have been received or lost. We argue that some changes must be made in the transport layer, to determine whether to resend a packet based on the packet's impact on DNN's inference dependent on which packets have been received. While much research is needed toward an optimal design of DNN-driven transport layer, we believe that we have taken the first step in reducing streaming delay while maintaining a high inference accuracy.