# Understanding Performance of Edge Content Caching for Mobile Video Streaming

## TL;DR

This paper analyzes real-world mobile video streaming data to evaluate and improve edge content caching strategies at Wi-Fi and cellular access points, revealing request patterns, user behaviors, and proposing an optimized caching method.

## Contribution

It provides the first comprehensive analysis of mobile video request patterns and user behaviors using large-scale real-world data, and designs an improved caching strategy based on these insights.

## Key findings

- Wi-Fi and cellular caching effectiveness varies with request patterns.
- User mobility significantly impacts caching performance.
- Proposed caching strategy outperforms traditional methods in experiments.

## Abstract

Today's Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3/4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks--from regional CDN servers to peer CDN servers (e.g., smartrouters in users' homes)--to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world datasets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over 2 weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors. Using trace-driven experiments, we compare strategies for edge content caching including LRU and LFU, in terms of supporting mobile video requests. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance.