Collaborative Multi-bitrate Video Caching and Processing in Mobile-Edge Computing Networks (1612.01436v2)

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 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.

• The paper formulates joint caching and processing in MEC as an ILP and proposes an online algorithm minimizing backhaul traffic without relying on popularity data.
• It introduces a collaborative strategy using edge transcoding between video bitrates to enhance cache hit ratios and reduce processing load.
• Simulations demonstrate significant improvements in cache hit ratios, backhaul traffic, and access delays compared to traditional video caching methods.

Overview of Multi-bitrate Video Caching in Mobile-Edge Computing Networks

The paper "Collaborative Multi-bitrate Video Caching and Processing in Mobile-Edge Computing Networks" presents a detailed examination of collaborative strategies for caching and processing video streams in Mobile-Edge Computing (MEC) networks. MEC is increasingly crucial in extending cloud-computing capabilities to the edge of Radio Access Networks (RANs), thus facilitating efficient data processing at the network periphery. This paper outlines a method for enhancing video streaming through a novel collaborative approach combining adaptive bitrate (ABR) streaming and edge processing.

Research Context

The context for this research stems from the need for efficient video streaming given the growing demand from Over-The-Top (OTT) service providers like Netflix and YouTube, contributing to the surge in mobile data traffic. Traditional video caching methods, which involve "store and transmit" mechanisms, are challenged by the varied user capabilities and fluctuating network conditions characteristic of mobile networks. While many existing solutions lack ABR awareness, this research introduces a strategy that leverages MEC for both caching and processing, focusing on the transcoding relationships between different video bitrate versions.

Methodology

The research formulates the problem of joint caching and processing as an Integer Linear Program (ILP), aiming to minimize the costs associated with backhaul network traffic while adhering to cache storage and processing constraints. Due to the NP-completeness of the ILP, the paper proposes an online algorithm that employs real-time cache placement and scheduling, based on the arrival of new video requests. This solution does not rely on pre-existing information about content popularity, unlike many offline approaches.

Key Contributions

1. Collaborative Caching and Processing: The strategy considers both caching multiple bitrate versions and utilizes transcoding capabilities at MEC servers, enhancing the cache hit ratio and reducing the processing load across the network.
2. Heuristic Online Algorithm: The proposed online algorithm dynamically handles video requests, leveraging the Least Recently Used (LRU) policy to adapt cache placement on-the-fly without needing prior data on content popularity.
3. Performance Evaluation: Extensive simulations show significant improvements in cache hit ratios and reductions in both backhaul traffic and initial access delays compared to traditional methods.

Implications and Future Directions

The strategy proposed in the paper has practical implications for content delivery networks and telecom operators facing the challenges of efficiently managing QoE-focused video streaming services. By improving cache utilization and reducing access delays, MEC-enabled networks can better support high-demand multimedia applications. Theoretical contributions include alternative approaches to ILP problems in distributed systems.

Future directions might involve enhancing the algorithm's sophistication to include cooperative transmission technologies like Coordinated Multi-Point (CoMP) and exploring more granular transcoding cost models based on device-specific parameters. Additionally, incorporating machine learning techniques to predict and adapt caching strategies dynamically could further optimize MEC network efficiency.

This research provides a robust framework for utilizing MEC in managing video caching and processing, illustrating the system's potential for supporting advanced multimedia services amid increasing mobile data demands. The online algorithm offers practical benefits by aligning real-time request handling with resource constraints, thereby benefiting both network operators and end-users.