Adaptive Bit Rate (ABR) streaming has become a popular video delivery technique, credited with improving Quality of Experience (QoE) of videos delivered on wireless networks. Recent independent research reveals video caching in the Radio Access Network (RAN) holds promise for increasing the network capacity and improving video QoE. In this paper, we investigate opportunities and challenges of combining the advantages of ABR and RAN caching to increase the video capacity and QoE of the wireless networks. While each ABR video is divided into multiple chunks that can be requested at different bit rates, a cache hit requires the presence of a specific chunk at a desired bit rate, making ABR-aware RAN caching challenging. To address this without having to cache all bit rate versions of a video, we propose adding limited processing capacity to each RAN cache. This enables transrating a higher rate version that may be available in the cache, to satisfy a request for a lower rate version, and joint caching and processing policies that leverage the backhaul, caching, and processing resources most effectively, thereby maximizing video capacity of the network. We also propose a novel rate adaptation algorithm that uses video characteristics to simultaneously change the video encoding and transmission rate. The results of extensive statistical simulations demonstrate the effectiveness of our approaches in achieving significant capacity gain over ABR or RAN caching alone, as well as other ways of enabling ABR-aware RAN caching, while improving video QoE.

As the video resolution, storage, and rendering capabilities of mobile devices improve, consumers tend to watch more videos on their devices. To address this increasing demand, there is a need to improve the capacity of cellular networks while also improving video Quality of Experience (QoE). Ensuring video QoE via wireless links remains a challenge, not only due to the backhaul demand, but also due to the varying wireless channel conditions caused by fading, multiuser interference, and peak traffic loads. Here, we introduce a reactive Mobile Device Caching (rMDC) framework to improve video capacity (number of concurrent video viewing sessions) and QoE in the presence of the challenges explained above. Using rMDC, a mobile device caches videos reactively as it requests them, evicts the videos least likely to be requested according to its neighbors aggregate User Preference Profile (UPP), and sharing video contents using D2D communication. We use a discrete event statistical simulation framework to study the performance of rMDC. Our simulation results demonstrate the effectiveness of rMDC, along with UPP-based caching, in achieving higher capacity and better QoE compared to no mobile device caching.