MEC Proxy for Efficient Cache and Reliable Multi-CDN Video Distribution

Viola, R.; Martín, Ángel; Zorrilla, Mikel; Montalbán, Jon

doi:10.1109/bmsb.2018.8436904

Cited by 22 publications

(8 citation statements)

References 8 publications

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“…Assuming that the video content provided by the MEC system has I kind of video content, the i kind of video is represented by v i . Each kind of video is encoded at a constant bit rate and serves at the same bit rate [10,11]. Assuming that the total number of MEC streaming media servers is J, the j server is represented by M j , and D is defined as the video deployment matrix with the size of I × J, and the element d ij ∈ f0, 1g represents whether or not a copy of v i is deployed on M j .…”

Section: Session Scheduling Strategy Based On Deep Reinforcement Learningmentioning

confidence: 99%

Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

Liu

2021

Wireless Communications and Mobile Computing

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In the resource scheduling of streaming Media Edge Cloud (MEC), in order to balance the cost and load of migration, this paper proposes a video stream session migration method based on deep reinforcement learning in cloud computing environment. First, combined with the current popular OpenFlow technology, a novel MEC architecture is designed, which separates streaming media service processing in application layer from forwarding path optimization in network layer. Second, taking the state information of the system as the attribute feature, the session migration is calculated, and gradient reinforcement learning is combined with in-depth learning and deterministic strategy for video stream session migration to solve the user request access problem. The experimental results show that the method has a better request access effect, can effectively improve the request acceptance rate, and can reduce the migration cost, while shortening the running time.

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Section: Session Scheduling Strategy Based On Deep Reinforcement Learningmentioning

confidence: 99%

Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

Liu

2021

Wireless Communications and Mobile Computing

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“…Thus, new solutions for improving multi-CDN delivery involve investigating MEC services. In [21] a MEC proxy is proposed. The proxy can retrieve video streaming metrics of video players at the access point transparently and CDNs performance metrics from the wired link.…”

Section: A Cdn Resource Selectionmentioning

confidence: 99%

Predictive CDN Selection for Video Delivery Based on LSTM Network Performance Forecasts and Cost-Effective Trade-Offs

Viola

Martín

Morgade

et al. 2021

IEEE Trans. on Broadcast.

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Owing to increasing consumption of video streams and demand for higher quality content and more advanced displays, future telecommunication networks are expected to outperform current networks in terms of key performance indicators (KPIs). Currently, content delivery networks (CDNs) are used to enhance media availability and delivery performance across the Internet in a cost-effective manner. The proliferation of CDN vendors and business models allows the content provider (CP) to use multiple CDN providers simultaneously. However, extreme concurrency dynamics can affect CDN capacity, causing performance degradation and outages, while overestimated demand affects costs. 5G standardization communities envision advanced network functions executing video analytics to enhance or boost media services. Network accelerators are required to enforce CDN resilience and efficient utilization of CDN assets. In this regard, this study investigates a cost-effective service to dynamically select the CDN for each session and video segment at the Media Server, without any modification to the video streaming pipeline being required. This service performs time series forecasts by employing a Long Short-Term Memory (LSTM) network to process real time measurements coming from connected video players. This service also ensures reliable and cost-effective content delivery through proactive selection of the CDN that fits with performance and business constraints. To this end, the proposed service predicts the number of players that can be served by each CDN at each time; then, it switches the required players between CDNs to keep the (Quality of Service) QoS rates or to reduce the CP's operational expenditure (OPEX). The proposed solution is evaluated by a real server, CDNs, and players and delivering dynamic adaptive streaming over HTTP (MPEG-DASH), where clients are notified to switch to another CDN through a standard MPEG-DASH media presentation description (MPD) update mechanism.

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“…Jointly minimizing the peak population of unserved clients and the number of replicas, the proposed caching algorithm obtained better delay reduction and cache utilization. Reference [16] studied how to minimize the traffic between content delivery network (CDN) and network edge, and support transparently and dynamically switch between multiple CDNs to keep QoS rates. Reference [17] solved a joint optimization problem of caching and transcoding for video on demand (VoD) services in MEC systems.…”

Section: Related Workmentioning

confidence: 99%

Deep-Q-Network-Based Multimedia Multi-Service QoS Optimization for Mobile Edge Computing Systems

et al. 2019

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By offloading storage and computing resources to the edge of networks, mobile edge computing (MEC) is emerged as a promising architecture to reduce the transmission delay and bandwidth waste for mobile multimedia services. This paper focuses on a multi-service scenario in the MEC systems, where the MEC server can provide three multimedia services including live streaming, buffered streaming and low latency enhanced mobile broadband applications for edge users at the same time. In order to satisfy various quality of service (QoS) requirements for different multimedia applications, the 5G QoS model is applied. Notably, the packets from the multimedia applications with the same or similar requirements are mapped into the same QoS flow, and each QoS flow is processed individually. Therefore, how to effectively schedule the limited radio resource for QoS flows is an intractable problem. To address the problem above, a QoS evaluation model is designed, and a QoS maximization problem is formulated. Furthermore, a deep reinforcement learning method, deep-Q-network, is adopted to make decisions to allocate radio resource dynamically. Compared with round-robin and priority-based scheduling algorithms, the simulation results validate that the proposed algorithm outperforms other resource scheduling algorithms for multi-service scenario. INDEX TERMS Deep-Q-network, deep reinforcement learning, mobile edge computing, multimedia, quality of service.

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MEC Proxy for Efficient Cache and Reliable Multi-CDN Video Distribution

Cited by 22 publications

References 8 publications

Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

Predictive CDN Selection for Video Delivery Based on LSTM Network Performance Forecasts and Cost-Effective Trade-Offs

Deep-Q-Network-Based Multimedia Multi-Service QoS Optimization for Mobile Edge Computing Systems

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