Multi-access edge computing (MEC) enables placing video content at the edge of the network aiming to improve the quality of experience (QoE) of the mobile clients. Video content caching at edge servers also reduces traffic in the backhaul of the mobile network, hence reducing operational costs for mobile network operators (MNOs). However, minimizing the rate of cache misses and maximizing the average video quality may sometimes be at odds with each other, particularly when the cache size is constrained. Our objective in this article is two fold: First, we explore the impact of fixed video content caching on the optimal QoE of mobile clients in a setup where servers at mobile network edge handle bitrate selection. Second, we want to investigate the effect of cache replacement on QoE-traffic trade-off. An integer nonlinear programming (INLP) optimization model is formulated for the problem of jointly maximizing the QoE, the fairness as well as minimizing overall data traffic on the origin video server. Due to its NP-Hardness, we then present a low complexity greedy-based algorithm with minimum need for parameter tuning which can be easily deployed. We show through simulations that the joint optimization indeed enables striking a desired trade-off between traffic reduction and QoE. The results also reveal that with fixed cached contents, the impact of caching on the QoE is proportional to the desired operational point of MNO. Furthermore, the effect of cache replacement on QoE is less noticeable compared to its effect on backhaul traffic when cache size is constrained.
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 back-haul, 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.