Trust and Reputation Multiagent-driven Model for Distributed Transcoding on Fog-Edge

Adaptive Bitrate Streaming is a popular technique for providing video media over the Internet. Nevertheless, the computational cost of transcoding a video in many formats can limit its application on live video streaming. Besides, the network overhead of transmitting simultaneously many versions of the same content is a problem. Offloading the transcoding job to the network edge can deal with the problem. Users and providers of live video could benefit from a joint scheme that allowed edge devices to do the transcoding with tolerable latency and delay. This work presents a multiagent-driven model to deal with the problem of distributed transcoding on fog-edge computing. Agents have well-defined roles relating to Broker, Transcoder, and Viewer Proxy. Trust and Reputation metrics derived from utility functions that take into account users’ quality of experience (QoE) are defined and applied. The Reputation-based Node Selection (ReNoS) algorithm is presented for selecting the best nodes to perform the transcoding tasks. The conducted experiments indicate that the proposed approach can afford utility gain keeping viewers’ QoE having the potential to be applied in real edge computing environments.