Joint Video Caching and Processing for Multi-Bitrate Videos in Ultra-Dense HetNets

Abstract

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 paper, 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.Index Terms-Video caching, video transcoding, multiple bitrate video, mobile edge computing (MEC), submodularity.

CD-LwTE: Cost- and Delay-Aware Light-Weight Transcoding at the Edge

“…Zhang et al [12] proposed a model to serve the clients' requests by video caching, transcoding, and fetching from the origin server, aiming at minimizing the average serving delay. They formulated the problem as a mixed-integer bilinear problem by considering resource constraints, i.e., storage, computation, and bandwidth, at the edge server.…”

“…In line 1, we sort the input set clusters based on the popularity attribute in O(x × log(x)) time, where x denotes the number of sub-clusters and equals x = m × r. Then the proposed algorithm (Alg. 4) calls the CostFunc() function l times (the number of threshold values in T r) in the worst case (lines [8][9][10][11][12][13][14]. The CostFunc() function determines the solution for each sub-cluster; thus, its time complexity is O(x) in the worst case, in which all sub-clusters have at least one segment/bitrate.…”

A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challenges

“…The BS then compete with each other to maximise the availed resources from the MEC at fixed price. A similar approach to [169] is proposed in [170] with additionally considering the user association. The problem is formulated as a mixed integer programming (MIP) to minimize the video retrieval latency in ultra dense heterogeneous networks.…”