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Emerging distributed cloud architectures, e.g., fog and mobile edgecomputing, are playing an increasingly important role in the efficient deliveryof real-time stream-processing applications (also referred to as augmentedinformation services), such as industrial automation and metaverse experiences(e.g., extended reality, immersive gaming). While such applications requireprocessed streams to be shared and simultaneously consumed by multipleusers/devices, existing technologies lack efficient mechanisms to deal withtheir inherent multicast nature, leading to unnecessary traffic redundancy andnetwork congestion. In this paper, we establish a unified framework fordistributed cloud network control with generalized (mixed-cast) traffic flowsthat allows optimizing the distributed execution of the required packetprocessing, forwarding, and replication operations. We first characterize theenlarged multicast network stability region under the new control framework(with respect to its unicast counterpart). We then design a novel queuingsystem that allows scheduling data packets according to their currentdestination sets, and leverage Lyapunov drift-plus-penalty control theory todevelop the first fully decentralized, throughput- and cost-optimal algorithmfor multicast flow control. Numerical experiments validate analytical resultsand demonstrate the performance gain of the proposed design over existingnetwork control policies.

Decentralized Control of Distributed Cloud Networks with Generalized Network Flows