EMS: Erasure-Coded Multi-Source Streaming for UHD Videos Within Cloud Native 5G Networks

Pu, Lingjun; Shi, Jianxin; Yuan, Xinjing; Chen, Xu; Jiao, Lei; Tian, Zhen; Xu, Jingdong

doi:10.1109/tmc.2023.3238356

Cited by 2 publications

(2 citation statements)

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“…Besides, proper and updated video segments need to be supplied from the cloud storage systems to achieve the features. In this regard, Recent studies focus on several methods of mobile and web streaming [1], [2], [15], [16], gateway-based shaping methods for HTTP adaptive stream-ing (HAS) [17], quality of experience of HAS [18], optimal transcoding and caching for adaptive streaming in content delivery networks [19], etc. However, none of these studies focuses on video streaming support for a cloud specifically for an Object Storage System, which is now treated as a well-adopted solution for cloud service development.…”

Section: B Sustainability Concern: Middleware Placementmentioning

confidence: 99%

See 1 more Smart Citation

RemOrphan: Object Storage Sustainability Through Removing Offline-Processed Orphan Garbage Data

Noor,

Al-Nabhan,

Islam

2023

IEEE Access

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Nowadays, extremely large amounts of structured and unstructured types of data are stored in public, private, and hybrid cloud storage using object storage systems. Among these, storing multimedia data such as image, video, and audio pose unique challenges and long-term effects on object storage Sustainability. Three such challenges are smoother and more efficient video streaming, middleware placement for media processing, and lastly, management of orphan garbage data. In order to tackle these challenges, this paper presents a generalized architecture for smooth and efficient management as well as retrieval of multimedia data in cloud systems. To do so, first, we propose a new middleware package in the object server for supporting smooth video streaming and on-demand playable video segment. Here, we demonstrate that video segment download time improves by up to 30% when segmentation is done in the object server rather than in the proxy server. After, we focus on how to find orphan garbage data on media cloud storage and to what extent they can hamper data retrieval. Specifically, we present a generalized architecture named 'RemOrphan' for detecting the orphan garbage data using OpenStack Swift hash Ring and scripts. We deploy a private media cloud SPMS and find that around 35% data can be orphan garbage data. Due to the huge amount of orphan data, rsync replication needs higher time and more network overhead which hampers the system's sustainability. We lower around 25% sync delay and 30% network overhead after deploying a deletion daemon to remove the orphan garbage data.

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Section: B Sustainability Concern: Middleware Placementmentioning

confidence: 99%

“…Furthermore, study [2] has developed a new framework called EMS for streaming ultra-high-definition video. This framework combines erasure-coded storage with multisource streaming.…”

Section: A On-demand Video Segmentation and Middleware Placementmentioning

confidence: 99%