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Recently, the growing demand for rich multimedia content such as Video onDemand (VoD) has made the data transmission from content delivery networks(CDN) to end-users quite challenging. Edge networks have been proposed as anextension to CDN networks to alleviate this excessive data transfer throughcaching and to delegate the computation tasks to edge servers. To maximize thecaching efficiency in the edge networks, different Mobile Edge Computing (MEC)servers assist each others to effectively select which content to store and theappropriate computation tasks to process. In this paper, we adopt acollaborative caching and transcoding model for VoD in MEC networks. However,unlike other models in the literature, different chunks of the same video arenot fetched and cached in the same MEC server. Instead, neighboring serverswill collaborate to store and transcode different video chunks and consequentlyoptimize the limited resources usage. Since we are dealing with chunks cachingand processing, we propose to maximize the edge efficiency by studying theviewers watching pattern and designing a probabilistic model where chunkspopularities are evaluated. Based on this model, popularity-aware policies,namely Proactive caching policy (PcP) and Cache replacement Policy (CrP), areintroduced to cache only highest probably requested chunks. In addition to PcPand CrP, an online algorithm (PCCP) is proposed to schedule the collaborativecaching and processing. The evaluation results prove that our model andpolicies give better performance than approaches using conventional replacementpolicies. This improvement reaches up to 50% in some cases.

Proactive Video Chunks Caching and Processing for Latency and Cost Minimization in Edge Networks