QoE-DASH: DASH QoE Performance Evaluation Tool for Edge-Cache and Recommendation

With the easy access to mobile networks and the proliferation of video applications, video traffic is occupying a great portion of the network traffic, which poses a new challenge of how to alleviate the heavy backhaul traffic and ensure the high quality of experience for video services. As a promising solution towards addressing this challenge, video caching in edge networks has recently received significant attention, which mostly considers the video popularity and the user preference for the video. However, few studies consider the user behavior and the user preference for different parts of the video that indeed have an essential impact on caching efficiency. Hence, this paper proposes a new caching and resource scheduling scheme for adaptive bitrate videos by incorporating these fine-grained factors. We first model the video service problem as a nonlinear integer programming problem, which can be divided into a cache placement problem and an online resource scheduling problem. Then we design efficient algorithms based on several techniques, including greedy strategy, relaxation and rounding, to solve the two problems. Extensive experimental results based on two real-world datasets show that the proposed solution achieves superior performance compared with several state-of-the-art caching approaches.

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QoE-DASH: DASH QoE Performance Evaluation Tool for Edge-Cache and Recommendation

Cited by 3 publications

References 17 publications

QoE-Aware Analysis and Management of Multimedia Services in 5G and Beyond Heterogeneous Networks

QoE-Aware Analysis and Management of Multimedia Services in 5G and Beyond Heterogeneous Networks

Video Streaming and Cloud Gaming Services Over 4G and 5G: A Complete Network and Service Metrics Dataset

Fine-grained Caching and Resource Scheduling for Adaptive Bitrate Videos in Edge Networks

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