Multi-stream Adaptive Offloading of Joint Compressed Video Streams, Feature Streams, and Semantic Streams in Edge Computing Systems

Edge computing (EC) is a promising paradigm for serving latency-sensitive video applications. However, massive compressed video transmission and analysis require considerable bandwidth and computing resources, posing enormous challenges for current multimedia frameworks. Novel multi-stream frameworks that incorporate feature streams are more practical. The reason is that feature streams containing compact video frame feature data have a lower bitrate and better serve machine vision tasks. Nevertheless, feature extraction by devices increases the latency and energy consumption of local computing. Therefore, how to offload suitable streams according to video task requirements and system resources is a challenging issue. This paper studies EC-based multi-stream adaptive offloading. We model the multi-stream offloading and computation problem to maximize system utility by jointly optimizing offloading decisions, computation resource allocation, and video frame sampling rates. Frame sampling rates, processing latency, and energy consumption are considered in system utility modeling. The formulated optimization problem is a mixed-integer programming (MIP) problem. We propose an efficient algorithm to address this MIP problem. The proposed algorithm relies on the Hungarian algorithm and improved greedy Markov approximation. The simulation results validate our proposed algorithm's superior performance.