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

Autor(es)

Esper, Joao Paulo ; Bastos Loureiro Moncao, Ana Claudia ; Chaves Rodrigues, Karlla B. ; Both, Cristiano Bonato ; Correa, Sand Luz ; Cardoso, Kleber Vieira ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022); v. N/A, p. 6-pg., 2022-01-01.

Resumo

The converging ecosystem provided by Multi-access Edge Computing (MEC) has motivated novel DASH video streaming provisioning scenarios involving the joint coordination of different mechanisms for caching, communication and control. Given the complexity of designing such mechanisms, it is important to provide the research community with open-source tools that support the assessment of their feasibility, specially in real-world environment. Current network emulators still require a significant programming effort to meeting this need. To fill this gap, a new DASH emulator called QoE-DASH is presented in this work. QoE-DASH builds upon goDASH to evaluate the QoE of users consuming DASH content, taking into account network properties, user preferences, and context information. To demonstrate the capabilities of QoE-DASH, we exercise different functionalities of our tool and present a case study where two joint caching and recommendation models, proposed in the literature, are evaluated and their effects on user QoE are depicted using state-of-the-art QoE metrics. (AU)

• Processo FAPESP: 18/23097-3 - SFI2: fatiamento de infraestruturas de internet do futuro
• Beneficiário: Tereza Cristina Melo de Brito Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Temático