Bitrate Adaptation for Video Streaming Services in Edge Caching Systems

Tran, Anh-Tien; Dao, Nhu‐Ngoc; Cho, Sungrae

doi:10.1109/access.2020.3011517

Cited by 16 publications

(5 citation statements)

References 23 publications

(25 reference statements)

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“…Different variants of the two strategies are found in current state-of-the-art, e.g. for random in [21], [36]- [38] and for greedy in [19], [20], [22], [34], [35]. For our performance comparisons, we have considered the following variants in view of the ZOMKP formulation.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In the sequel, we term the proposed strategy as the Bound and Bound 0/1 Multiple Knapsack Problem (BB-ZOMKP) strategy and present it in the pseudocode form of Algorithm IV-B. The proposed BB-ZOMKP strategy consist of four main parts: Initialize (steps 4-8), BaselinePlacement (steps 9-24), ContentPlacement (steps [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] and Backtrack (steps 41-56).…”

Section: ) Proposed Bound-and-bound Content Placement Strategymentioning

confidence: 99%

“…Recall that by construction MHs are ordered in ascending cache size and files are ordered in descending 'value per unit size'. Every time a file m is placed (fixed) into the cache of MH n (steps 30-32), the upper CHP performance bound is re-calculated (step 33) and if it is lower than the current CHP performance ψ (step 34), part of the solution will be backtracked using the Backtrack part of the algorithm (steps [34][35][36]. The procedure resumes after corrective measures are taken using the Backtrack logic, potentially running recursively part of the strategy again to identify a better content placement solution.…”

Section: ) Proposed Bound-and-bound Content Placement Strategymentioning

confidence: 99%

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Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

et al. 2021

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Multimedia content delivery through the cellular infrastructure increases fast due to the enormous volumes of mobile video traffic generated by the billions of end devices populating the mobile data network. A critical mass of mobile video content requests refers to the consumption of the same popular video content, which is consumed by different end terminals spanning small geographical regions. Such content requests put a great burden on the backhaul of content-agnostic cellular networks, which fail to exploit the correlation of video requests to decongest their backhaul links. This creates redundant retransmissions while fetching the same video content from a central server to the network edge, using the bandwidth-limited backhaul at peak-time periods. With the integration of multi-access edge computing (MEC) capabilities in 5G mobile cellular networks, mobile network operators can place popular video content closer to the network edge at off-peak time periods, predicting user requests exhibiting a high correlation for a given time interval over smaller geographical regions. In this paper, we investigate popular content placement in multi-tier heterogeneous cellular networks where the edge network infrastructure can cooperate to create content delivery (and placement) clusters to effectively serve correlated video requests. To this end, we model the cooperative content placement problem using the multiple Knapsack (MKP) formulation and present an exact (optimal) bound-and-bound strategy to solve it. The performance of the proposed strategy is evaluated in-depth using extensive system-level simulations and is compared against that of other state-of-the-art algorithms. Valuable design guidelines and key performance trade-offs are discussed, paving the way towards cluster-based cooperative caching in MEC-enabled cellular network setups.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: ) Proposed Bound-and-bound Content Placement Strategymentioning

confidence: 99%

Section: ) Proposed Bound-and-bound Content Placement Strategymentioning

confidence: 99%

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Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

et al. 2021

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“…The cross-domain prediction models use features from external sources (e.g., attractiveness of the video on other sources) to increase the accuracy of the decision. In [158], authors improve the popularity-aware video caching by jointly considering the popularity and attractiveness of the video stream. The authors argue that despite the high popularity of the videos, user may decide to finish the videos after a short duration of viewing (i.e 15 seconds).…”

Section: ) Human Behavior-aware Strategiesmentioning

confidence: 99%

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

et al. 2022

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5G communication brings substantial improvements in the quality of service provided to various applications by achieving higher throughput and lower latency. However, interactive multimedia applications (e.g., ultra high definition video conferencing, 3D and multiview video streaming, crowdsourced video streaming, cloud gaming, virtual and augmented reality) are becoming more ambitious with high volume and low latency video streams putting strict demands on the already congested networks. Mobile Edge Computing (MEC) is an emerging paradigm that extends cloud computing capabilities to the edge of the network i.e., at the base station level. To meet the latency requirements and avoid the end-toend communication with remote cloud data centers, MEC allows to store and process video content (e.g., caching, transcoding, pre-processing) at the base stations. Both video on demand and live video streaming can utilize MEC to improve existing services and develop novel use cases, such as video analytics, and targeted advertisements. MEC is expected to reshape the future of video streaming by providing ultrareliable and low latency streaming (e.g., in augmented reality, virtual reality, and autonomous vehicles), pervasive computing (e.g., in real-time video analytics), and blockchain-enabled architecture for secure live streaming. This paper presents a comprehensive survey of recent developments in MEC-enabled video streaming bringing unprecedented improvement to enable novel use cases. A detailed review of the state-ofthe-art is presented covering novel caching schemes, optimal computation offloading, cooperative caching and offloading and the use of artificial intelligence (i.e., machine learning, deep learning, and reinforcement learning) in MEC-assisted video streaming services.

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“…On the other hand, some approaches [20,21] use both the popularity and retention rate of video streams to maximize video bitrate for efficient utilization of bandwidth. Distinguished from these approaches which emphasize bitrate adaptation, this paper focuses on the topology optimization for the cooperation of MEC servers while additionally considering the scheduling delay to achieve lower link capacity with delay constraints.…”

Section: Relate Workmentioning

confidence: 99%

Popularity-Guided Cost Optimization for Live Streaming in Mobile Edge Computing

Zhu

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Live streaming service usually delivers the content in mobile edge computing (MEC) to reduce the network latency and save the backhaul capacity. Considering the limited resources, it is necessary that MEC servers collaborate with each other and form an overlay to realize more efficient delivery. The critical challenge is how to optimize the topology among the servers and allocate the link capacity so that the cost will be lower with delay constraints. Previous approaches rarely consider server collaborations for live streaming service, and the scheduling delay is usually ignored in MEC, leading to suboptimal performances. In this paper, we propose a popularity-guided overlay model which takes the scheduling delay into consideration and utilizes MEC collaboration to achieve efficient live streaming service. The links and servers are shared among all channel streams and each stream is pushed from cloud servers to MEC servers via the trees. Considering the optimization problem is NP-hard, we propose an effective optimization framework called cost optimization for live streaming (COLS) to predict the channel popularity by a LSTM model with multiscale input data. Finally, we compute topology graph by greedy scheme and allocate the capacity with convex programming. Experimental results show that the proposed approach achieves higher prediction accuracy, reducing the capacity cost by more than 40% with an acceptable delay compared with state-of-the-art schemes.

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Bitrate Adaptation for Video Streaming Services in Edge Caching Systems

Cited by 16 publications

References 23 publications

Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

Cooperative Content Caching in MEC-Enabled Heterogeneous Cellular Networks

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

Popularity-Guided Cost Optimization for Live Streaming in Mobile Edge Computing

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