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Elastic optical network (EON) efficiently utilize spectral resources foroptical fiber communication by allocating the minimum necessary bandwidth toclient demands. On the other hand, network traffic has been continuouslyincreasing due to the wide penetration of video streaming services, so theefficient and cost-effective use of available bandwidth plays an important rolein improving service provisioning. In this work, we formulate and solve anoptimization problem to perform routing and spectrum assignment (RSA) in EONwith focus on video streaming. In this formulation, EON and video constraintssuch as spectrum fragmentation and received video quality are consideredjointly. In this way, we utilize a machine learning (ML) technique to estimatethe video quality versus channel state. The proposed algorithm is evaluatedover two benchmarks fiber-optic network, namely NSFNET and US-backbone usingnumerical simulations based on random traffic models. The results reveal thatthe mean optical signal-to-noise ratio (OSNR) for video content data in thereceiver is remarkably higher than in non-video data. This is while theblocking ratio is the same for both data types.

Optimized Routing and Spectrum Assignment for Video Communication over an Elastic Optical Network