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.
Joint video caching and processing at the edge of the network has also been studied. @cite_26 propose the joint optimization of adaptive video streaming, backhaul resource allocation, and video content caching at RAN. The proposed adaptive video streaming and caching model in this work assumes that the clients request to the chunks with lower bitrates may be served from the cache by transcoding from the available chunks with higher bitrates at the edges. To efficiently utilize the potential of joint adaptive video streaming and caching, they also propose a proactive user preference profile (UPP)-based cache replacement strategy. However, in scenarios with limited processing capabilities at the edge servers and high bandwidth capacity on the backhaul network, the encoding of the video chunks can be performed at the origin server while the caching and clients information processing are handled at the edges. Furthermore, the proposed cache replacement strategy in @cite_26 takes into account only the video popularity and bitrate estimation, while, some statistical information about the clients departure (retention) toward different videos is another factor that can be utilized to improve the LRU-based cache replacement strategies.
@cite_26
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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.