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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Today’s Internet has witnessed an increase in the popularity of mobile video streaming, which is expected to exceed 3 4 of the global mobile data traffic by 2019. To satisfy the considerable amount of mobile video requests, video service providers have been pushing their content delivery infrastructure to edge networks—from regional content delivery network (CDN) servers to peer CDN servers (e.g., smartrouters in users’ homes)—to cache content and serve users with storage and network resources nearby. Among the edge network content caching paradigms, Wi-Fi access point caching and cellular base station caching have become two mainstream solutions. Thus, understanding the effectiveness and performance of these solutions for large-scale mobile video delivery is important. However, the characteristics and request patterns of mobile video streaming are unclear in practical wireless network. In this paper, we use real-world data sets containing 50 million trace items of nearly 2 million users viewing more than 0.3 million unique videos using mobile devices in a metropolis in China over two weeks, not only to understand the request patterns and user behaviors in mobile video streaming, but also to evaluate the effectiveness of Wi-Fi and cellular-based edge content caching solutions. To understand the performance of edge content caching for mobile video streaming, we first present temporal and spatial video request patterns, and we analyze their impacts on caching performance using frequency-domain and entropy analysis approaches. We then study the behaviors of mobile video users, including their mobility and geographical migration behaviors, which determine the request patterns. Using trace-driven experiments, we compare strategies for edge content caching, including least recently used (LRU) and least frequently used (LFU), in terms of supporting mobile video requests. We reveal that content, location, and mobility factors all affect edge content caching performance. Moreover, we design an efficient caching strategy based on the measurement insights and experimentally evaluate its performance. The results show that our design significantly improves the cache hit rate by up to 30 compared with LRU LFU. We propose a novel MIMO cooperation framework called the cache-induced opportunistic cooperative MIMO (Coop-MIMO) for video streaming in backhaul limited multicell MIMO networks. By caching a portion of the video files, the base stations (BSs) opportunistically employ Coop-MIMO transmission to achieve MIMO cooperation gain without expensive payload backhaul. We derive closed form expressions of various video streaming performance metrics for cache-induced opportunistic Coop-MIMO and investigate the impact of BS level caching and key system parameters on the performance. Specifically, we first obtain a mixed fluid-diffusion limit for the playback buffer queueing system. Then we derive the approximated video streaming performance using the mixed fluid-diffusion limit. Based on the performance analysis, we formulate the joint optimization of cache control and playback buffer management as a stochastic optimization problem. Then we derive a closed form solution for the playback buffer thresholds and develop a stochastic subgradient algorithm to find the optimal cache control. The analysis shows that the video streaming performance improves linearly with the BS cache size BC, the transmit power cost decreases exponentially with BC, and the backhaul cost decreases linearly with BC. 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 videoaware wireless channel scheduler results in significantly (up to 250 ) higher video capacity with very low stalling probability. To alleviate the backhaul burden and reduce user-perceived latency, content caching at base stations has been identified as a key technology. However, the caching strategy design at the wireless edge is challenging, especially when both wired backhaul condition and wireless channel quality are considered in the optimization. In this paper, taking into account the conditions of the backhaul in terms of delay and wireless channel quality, joint design and optimization of the caching and user association policy to minimize the average download delay is studied in a cache-enabled heterogeneous network. We first prove the joint caching and association optimization problem is NP-hard based on a reduction to the facility location problem. Furthermore, in order to reduce the complexity, a distributed algorithm is developed by decomposing the NP-hard problem into an assignment problem solvable by the Hungarian method and two simple linear integer subproblems, with the aid of McCormick envelopes and the Lagrange partial relaxation method. Simulation results reveal a near-optimal performance that performs up to 22 better in term of delay compared with those in the literatures at a low complexity of @math .