Caching popular videos at the edge has been confirmed as a promising way to support low-latency video transmission and alleviate the backhaul traffic burden. Meanwhile, mobile edge computing (MEC) has also been regarded as an effective solution to meet the 5G low-latency service requirements. In this article, we propose to fully utilize both the storage and computing resources at edge servers to support multiple bitrate video streaming. We design the video caching, processing, and user association models that aim to minimize the average retrieval latency of all users. This problem is modeled as a mixed-integer bilinear problem, which is NP-hard . We show that under practical constraints on storage, bandwidth, and processing capacity, the problem does not exhibit sub-modular property and the performance of a greedy algorithm may not be strictly guaranteed. To deal with this challenging problem, we decompose the original problem into a cache placement problem and a user-BS association problem, while still preserving the interplay between the two sub-problems. A linearization and rounding algorithm, including: (i) a greedy rounding proactively caching scheme and (ii) a random-rounding user-BS association scheme, is then proposed, with performance bounds derived. Extensive simulation results show that the proposed scheme can achieve a near-optimal performance under various storage, computing capacity, and downlink bandwidth settings.

中文翻译：

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超密集HetNet中用于多比特率视频的联合视频缓存和处理

已经证实，在边缘缓存高速视频是支持低延迟视频传输并减轻回程流量负担的一种有前途的方法。同时，移动边缘计算（MEC）也被视为满足5G低延迟服务要求的有效解决方案。在本文中，我们建议充分利用边缘服务器上的存储和计算资源来支持多种比特率视频流。我们设计了视频缓存，处理和用户关联模型，旨在最小化所有用户的平均检索延迟。该问题被建模为混合整数双线性问题，即NP硬 。我们表明，在存储，带宽和处理能力的实际限制下，该问题不会表现出亚模块化性质，并且可能无法严格保证贪婪算法的性能。为了解决这个具有挑战性的问题，我们将原始问题分解为缓存放置问题和用户-BS关联问题，同时仍然保留了两个子问题之间的相互作用。然后提出一种线性化和舍入算法，包括：（i）贪婪舍入主动缓存方案和（ii）随机舍入用户-BS关联方案，并推导性能界限。大量的仿真结果表明，该方案在各种存储，计算能力和下行链路带宽设置下都能达到接近最佳的性能。