CoVStream: Edge-Cloud Collaboration for Understanding of Long Video Streams

A new framework called CoVStream proposes splitting the analysis of long video streams between edge devices and cloud servers, addressing a key bottleneck in multimedia intelligence where constrained hardware meets bandwidth-heavy cloud processing [1]. The framework, described in a preprint posted to arXiv on June 22, 2026, is presented as the first edge-cloud collaborative system designed specifically for understanding continuous video streams [1][2]. The authors, led by Xu Liu, argue that current methods force an impractical choice: offload raw video to the cloud for powerful reasoning at the cost of prohibitive bandwidth, or process it locally on devices with limited computational capacity [1][2]. CoVStream divides the workload. An edge node first distills the incoming raw video into compact visual features and semantic captions. This condensed representation is then transmitted to a cloud server, which integrates the data into an entity graph and a global visual context. The cloud's heavy reasoning model is activated only when a user query arrives, rather than running continuously on the full stream [1][2]. In experiments across three datasets—VideoMME-Long, LVBench, and RTV-Bench—the system reduced bandwidth usage by 87.6% while retaining 99.2% of the accuracy of a full-cloud baseline on the LVBench benchmark [1][2]. The submission file was 852 KB and was revised on June 25, 2026 [1]. The paper appears on arXiv, an open-access repository that hosts preprints across physics, mathematics, computer science, and related fields [6]. Founded in 1991, arXiv passed the two-million-article milestone by the end of 2021 and now receives about 24,000 submissions per month [6]. Preprints on the platform are moderated but not peer-reviewed, a status that has drawn attention in cases such as the 2023 LK-99 room-temperature superconductor claims, which were later found to lack evidence of superconductivity after widespread replication attempts [6][8]. arXiv also supports community-built tools through its arXivLabs framework, which allows third-party developers to create features such as citation explorers and code-finding services that appear on article pages [4][5]. The program, formalized in 2020, requires collaborators to adhere to values of openness, community, excellence, and user data privacy [5].