Combining Resource-Aware Recommendation and Caching in the Era of MEC for Improving the Experience of Video Streaming Users
| Autor: | Ana Claudia B. L. Monção, Sand Luz Correa, Aline Carneiro Viana, Kleber Vieira Cardoso |
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| Přispěvatelé: | Universidade Federal de Goiás [Goiânia] (UFG), inTeRnet BEyond the usual (TRiBE ), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Services Computing IEEE Transactions on Services Computing, 2022, pp.1-14. ⟨10.1109/TSC.2022.3205482⟩ |
| ISSN: | 2372-0204 1939-1374 |
| Popis: | International audience; The coupling between content caching at the wireless network edge and video recommendation systems has shown promising results to optimize the cache hit and improve the user quality of experience (QoE). However, the quality of the UE wireless link and the resource capabilities of the UE are aspects that impact user QoE and that have been neglected in the literature. In this work, we present a resource-aware optimization model for the joint task of caching and recommending videos to mobile users that maximizes the cache hit ratio and the user QoE under the constraints of UE capabilities and the availability of network resources. In order to make the problem manageable, we assume that the regular user consumes video content keeping some time interval between them, and this user moves slowly inside the coverage of a base station. We evaluate our proposal using a video catalog derived from a real-world video content dataset and real-world video representations and compare the performance with a state-of-the-art caching and recommendation method unaware of computing and network resources. Results show that our approach increases user QoE by at least 68% and cache hit ratio by at least 14% in comparison with the other method. |
| Databáze: | OpenAIRE |
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