Dynamic Optimization and Pricing of Transcoding Multicast in Edge Computing Networks

This work studies video transcoding and multicast in an edge computing network (ECN), where routers are furnished with computing resources. For example, the recent IPv6-SRv6 paradigm enables programmable networking that works in concert with such hardware innovations, provisioning application-defined route selection and realizing compute&forward functionalities. A prominent class of user applications in ECN is video streaming. Edge routers may replicate and transcode video streams while delivering them toward end users for customized services, up to node processing and link transmission capacities. We design an auction-based online optimization framework DOP, which comprises of three components: 1) video access management; 2) dynamic price function design; and 3) transcoding multicast tree (TMT) optimization. We formulate the online social welfare maximization problem, and design an efficient primal-dual framework that simultaneously makes video transcoding, multicast routing, and resource pricing decisions. Through rigorous theoretical analysis, we prove DOP guarantees truthfulness and achieves a good competition ratio. Extensive simulations further verify that social welfare increases by about 12.9%, in comparison to benchmark algorithms.