  Recently, Mobile-Edge Computing (MEC) has arisen as an emerging paradigm that
extends cloud-computing capabilities to the edge of the Radio Access Network
(RAN) by deploying MEC servers right at the Base Stations (BSs). In this paper,
we envision a collaborative joint caching and processing strategy for on-demand
video streaming in MEC networks. Our design aims at enhancing the widely used
Adaptive BitRate (ABR) streaming technology, where multiple bitrate versions of
a video can be delivered so as to adapt to the heterogeneity of user
capabilities and the varying of network connection bandwidth. The proposed
strategy faces two main challenges: (i) not only the videos but their
appropriate bitrate versions have to be effectively selected to store in the
caches, and (ii) the transcoding relationships among different versions need to
be taken into account to effectively utilize the processing capacity at the MEC
servers. To this end, we formulate the collaborative joint caching and
processing problem as an Integer Linear Program (ILP) that minimizes the
backhaul network cost, subject to the cache storage and processing capacity
constraints. Due to the NP-completeness of the problem and the impractical
overheads of the existing offline approaches, we propose a novel online
algorithm that makes cache placement and video scheduling decisions upon the
arrival of each new request. Extensive simulations results demonstrate the
significant performance improvement of the proposed strategy over traditional
approaches in terms of cache hit ratio increase, backhaul traffic and initial
access delay reduction.
