Popularity-Based Video Caching Techniques for Cache-Enabled Networks: A Survey

Abstract

The proliferation of the mobile Internet and connected devices, which offer a variety of services at different levels of performance is a major challenge for the fifth generation of wireless networks and beyond. Innovative solutions are needed to leverage recent advances in machine storage/memory, context awareness, and edge computing. Cache-enabled networks and techniques such as edge caching are envisioned to reduce content delivery times and traffic congestion in wireless networks. Only a few contents are popular, accounting for the majority of viewers, so caching them reduces the latency and download time. However, given the dynamic nature of user behavior, the integration of popularity prediction into caching is of paramount importance to better network utilization and user satisfaction. In this paper, we first present an overview of caching in wireless networks and then provide a detailed comparison of traditional and popularity-based caching. We discuss the attributes of videos and the evaluation criteria of caching policies. We summarize some of the recent work on proactive caching, focusing on prediction strategies. Finally, we provide insight into the potential opportunities and challenges as well as some open research problems enable the realization of efficient deployment of popularity-based caching as part of the next-generation mobile networks. INDEX TERMS 5G, cache-enabled networking, popularity prediction, proactive caching, videos popularity.

The role of caching in next generation cellular networks: A survey and research outlook

“…Most popular content (videos) MPV is an additional caching strategy used in content delivery networks. It caches the most popular video grounded on the global video distribution …”

“…Because the cache is close to the end user, edge caching can also reduce the content delivery delay. In the work of Goian et al, the pause and initial delay of video session are reduced by joint scheduling of wireless channel and return; thus, the video QoE is improved. Similarly, the tradeoff between delivery delays and implementation resources is also found in the work of Baev et al Using stochastic geometry, the authors analyzed cache‐enabled tier‐2 network with a traditional network without cache .…”

“…Identify numerous important aspects of the integration of computing, caching and networking motivations, performance metric, frameworks, enabling technologies, and challenges. Goian et al 63 A detailed comparison of popularity-based caching and traditional caching in wireless networks is presented. Also, summarized some of the recent work on proactive caching, focusing on prediction strategies.…”

Online Content Popularity Prediction and Learning in Wireless Edge Caching

“…With the continuous development of various intelligent devices such as smart vehicles, smart home appliances, mobile devices, etc, and various sized innovative application services such as news updates, high quality video feeds, software updates, etc., wireless mobile communications has been experiencing an unprecedented traffic surge with a lot of redundant and repeated information, which limits the capacity of the fronthaul and backhaul links [1]. To lower the redundant traffic, caching has emerged as an effective solution for reducing the peak data rates by pre-fetching the most popular contents in the local cache storage of the base stations (BS).…”

“…Transfer learning methods are used in [12] by leveraging content correlation and information transfer between time periods. To learn CP independently across contents, online policies are presented for cache-awareness in [13], low complexity video caching in [1], [14], user preference learning in [15], etc. These works are employed for a particular system with the fixed number of BSs and users, i.e., the statistical performance of the network as whole is lacking with respect to content delivery in the physical layer.…”