Collaborative multi-bitrate video caching and processing in Mobile-Edge Computing networks

Abstract

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 condition. 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 NPcompleteness 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.Index Terms-Collaborative caching; adaptive bitrate streaming; multi-bitrate video; mobile-edge computing; joint caching and processing.

Multi-access Edge Computing: A Survey

“…Tran et al [25] investigate MEC usability for a joint collaborative caching and processing problem (JCCP) for on-demand video streaming. Here, MEC computational capability is used for transcoding of a video to different bitrate version to satisfy user requests, so that users can receive videos that are suited for their network condition and capabilities of the user terminals.…”

Cooperative Multi-Bitrate Video Caching and Transcoding in Multicarrier NOMA-Assisted Heterogeneous Virtualized MEC Networks

“…In practical scenarios, due to the multiple bitrate variants of each unique video file, service providers often need to transcode video files into multiple bitrates [5]- [9]. To this end, adaptive bit rate (ABR) streaming techniques have been developed to enhance the quality of delivered video in radio access networks (RANs) where each video file is adjusted according to users' requests based on their display size and network channel conditions [5]- [7]. communications to maximize the user revenues.…”

“…One problem with this, however, is that duplicated video caching and transcoding in multiple resource-constrained MEC servers wastes both storage and processing resources. To tackle this issue, cooperative joint multi-bitrate video caching and transcoding (CVCT) technology is proposed where each MEC server is able to receive the requested video files from neighboring MEC servers via fronthaul links [7]. In this architecture, each MEC server is deployed side-by-side with each base station (BS) using the generic computing platforms which provides the caching and computation capabilities in heterogeneous networks (HetNets) [5]-[8].3 By sharing both the storage and processing resources among multiple MEC servers, more video files can be prefetched within RANs which results increasing the cache hit ratio [7], [8].…”

“…Exploiting MC-NOMA in virtualized networks can further reduce the wireless bandwidth cost of slices by reusing each subcarrier for multiple users owned by each slice. In this way, the integration of the aforementioned technologies enables the CVCT technology at MEC servers and network infrastructure abstraction for different cost-efficient wireless servicing.Transcoding a large number of videos at each resource-constrained MEC server simultaneously poses another challenge for delay-sensitive services [5]- [7]. The significant performance gain of the CVCT system can only be achieved when a joint distributed video caching and transcoding strategy is designed [7].…”

“…In this way, the integration of the aforementioned technologies enables the CVCT technology at MEC servers and network infrastructure abstraction for different cost-efficient wireless servicing.Transcoding a large number of videos at each resource-constrained MEC server simultaneously poses another challenge for delay-sensitive services [5]- [7]. The significant performance gain of the CVCT system can only be achieved when a joint distributed video caching and transcoding strategy is designed [7]. Accordingly, the major question is which bitrate variant of a video file should be cached or transcoded to another lower bitrate variant?…”