In this paper, we introduce distributed caching of videos at the base stations of the Radio Access Network (RAN) to significantly improve the video capacity and user experience of mobile networks. To ensure effectiveness of the massively distributed but relatively small-sized RAN caches, unlike Internet content delivery networks (CDNs) that can store millions of videos in a relatively few large-sized caches, we propose RAN-aware reactive and proactive caching policies that utilize User Preference Profiles (UPPs) of active users in a cell. Furthermore, we propose video-aware backhaul and wireless channel scheduling techniques that, in conjunction with edge caching, ensure maximizing the number of concurrent video sessions that can be supported by the end-to-end network while satisfying their initial delay requirements and minimize stalling. To evaluate our proposed techniques, we developed a statistical simulation framework using MATLAB and performed extensive simulations under various cache sizes, video popularity and UPP distributions, user dynamics, and wireless channel conditions. Our simulation results show that RAN caches using UPP-based caching policies, together with video-aware backhaul scheduling, can improve capacity by 300% compared to having no RAN caches, and by more than 50% compared to RAN caches using conventional caching policies. The results also demonstrate that using UPP-based RAN caches can significantly improve the probability that video requests experience low initial delays. In networks where the wireless channel bandwidth may be constrained, application of our video-aware wireless channel scheduler results in significantly (up to 250%) higher video capacity with very low stalling probability.

Adaptive Bit Rate Streaming (ABR) has become a popular video delivery technique, credited to improving the quality of delivered video on wireless networks. At the same time, recent research has shown video caching in the Radio Access Network (RAN) can be a promising way to increase capacity of the network while reducing video latency and improving video quality of experience. In this work, we investigate the opportunities and challenges of combining the advantages of ABR streaming and RAN caching to maximize the video capacity of wireless networks. Since with ABR, each video is divided into multiple segments, chunks, and each chunk can be requested at different bit rates, the caching requirements are very different and challenging; a cache hit will require not only the presence of a specific video chunk, but also the availability of the desired bit rate. One way to solve this problem is to cache all variants of a video, but this approach may significantly increase storage and backhaul requirements or reduce the number of unique videos that can be cached. In this paper, we introduce a framework consisting of rate adaptation algorithm, video caching and processing within the wireless cloud, with the aim to improve video capacity of the wireless network and satisfy or exceed QoE of each video request. To achieve this goal, we propose a new ABR algorithm, along with an ABR aware Least Recently Used (LRU) caching policy with Processing (ABRLRU-P) to support caching of video chunks with different bit rates. Using our MATLAB statistical simulation framework, we demonstrate a capacity improvement of up to 83% when ABR is used with RAN caching and processing using the ABR-LRU-P policy compared with using no RAN caching and video processing. Further, using ABR along with ABRLRU-P caching policy can improve the capacity by 68% compared with using ABR and a straightforward static LRU caching policy which fetches all video bit rate versions of a video chunk upon a cache miss.

We had introduced video caching techniques in the Radio Access Network (RAN) in [1] as a way to reduce the need to bring requested videos from Internet CDNs, thus reducing overall backhaul traffic, improving video quality of experience and increasing network capacity to support more simultaneous video requests. In this paper, we investigate supplementing the resulting wireless cloud with a hierarchical caching scheme, where the gateways in the Core Network (CN) also have video caches. The hierarchical caching approach further improves network capacity by enabling multiple cell sites to share caches at higher levels of the hierarchy, thereby improving overall cache hit ratio, without increasing the total cache size used. In addition, we exploit hierarchical caching to better accommodate mobility, so that when a user with an active video session moves from one cell to a neighboring cell, it is likely that the video currently being downloaded is already in a cache within the RAN or CN network associated with the new cell. To achieve the goal of improving capacity and supporting mobility, we extend our User Preference Profile (UPP) based caching policies [1] to accommodate the hierarchical caching structure introduced in this paper. For all the videos that miss the cache in any layer of hierarchy, we propose a scheduling approach to allocate RAN and CN backhaul resources judiciously so as to maximize the capacity of the wireless network. We extend our discrete event statistical simulation framework developed in [1] to study the performance of the proposed hierarchical caching approach. Our simulation results show that using hierarchical caching can enhance cache hit ratio by 24% and network capacity by up to 45% compared to caching only in the RAN. Significant capacity gains are also observed when additionally considering user mobility.

In this paper, we introduce distributed caching of videos at the base-stations of the Radio Access Network (RAN) as a way to reduce the need to bring requested videos from Internet CDNs, thereby reducing backhaul transmission, improving video quality of experience - delay and video stalling - and increasing overall network capacity to support more number of simultaneous video requests. Unlike Internet CDNs that can store millions of videos in a relatively few large sized caches, our proposed caching architecture consists of a very large number of micro-caches, with each base-station micro-cache being able to store only 1000s of videos, and hence may not be able to have high cache hit ratio. To address this challenge, we propose two new caching policies based on the User Preference Profile (UPP) of users in a cell: R-UPP (Reactive UPP) and P-UPP (Proactive UPP). Further, for videos that result in cache misses and need to be fetched from Internet CDNs, we develop a video scheduling approach that allocates the RAN backhaul resources to the video requests so as to reduce video latency and increase network capacity. We develop a discrete event statistical simulation framework using MATLAB to study the performance of RAN caching. Our simulation results show that RAN micro-caches with the proposed UPP-based caching policies, together with the proposed scheduling approach, can improve the probability of video requests that can meet initial delay requirements by almost 60%, and the number of concurrent video requests that can be served by up to 100%. The results also show that UPP based policies can enhance network capacity by up to 30% compared to conventional caching policies.