The proliferation of multi-resolution video streaming, driven by Adaptive Bitrate (ABR) technology, imposes significant storage and computational burdens on content delivery networks. While edge computing alleviates latency, the cost of storing and processing numerous video versions remains a key challenge. To address this, we propose UniStream, an optimization framework that unifies in-network video processing—including Super-Resolution (SR) and transcoding—with intelligent caching and fetching strategies in a collaborative edge-cloud architecture. UniStream dynamically determines the most cost-effective action for each video request, deciding whether to serve content from cache, generate it on-the-fly via SR or transcoding, or fetch it from a peer node or the central cloud. We formulate this complex resource allocation challenge as a Mixed-Integer Linear Programming (MILP) problem, optimizing for minimal total cost under realistic storage, compute, and latency constraints. Extensive experiments using real-world datasets demonstrate that UniStream significantly reduces operational costs compared to baseline methods while maintaining competitive delivery latency, proving highly effective across diverse user request patterns and network conditions.

Federated Learning (FL) is a key distributed learning paradigm challenged by model heterogeneity and temporal shifts in data distribution. Existing methods to these problems are often impractical for heterogeneous environments, as they typically require the exchange of model parameters or the use of auxiliary public datasets. To address these limitations, we propose FCLHet, a novel Federated Continual Learning framework designed for resource-constrained, Heterogeneous environments. The core of FCLHet is a server-side spatiotemporal knowledge cache that collects data features from clients. This cache, combined with an active selection strategy guided by a client-side policy network, enables mining and sharing of personalized knowledge subsets. This mechanism enables collaborative learning without sharing model parameters or auxiliary data, allowing heterogeneous clients to continually learn and adapt while mitigating catastrophic forgetting. Extensive experiments on four benchmark datasets show that FCLHet improves average accuracy by 2–5% while keeping communication overhead comparable to state-of-the-art federated distillation techniques.