  Conventional per-title encoding schemes strive to optimize encoding
resolutions to deliver the utmost perceptual quality for each bitrate ladder
representation. Nevertheless, maintaining encoding time within an acceptable
threshold is equally imperative in online streaming applications. Furthermore,
modern client devices are equipped with the capability for fast
deep-learning-based video super-resolution (VSR) techniques, enhancing the
perceptual quality of the decoded bitstream. This suggests that opting for
lower resolutions in representations during the encoding process can curtail
the overall energy consumption without substantially compromising perceptual
quality. In this context, this paper introduces a video super-resolution-based
latency-aware optimized bitrate encoding scheme (ViSOR) designed for online
adaptive streaming applications. ViSOR determines the encoding resolution for
each target bitrate, ensuring the highest achievable perceptual quality after
VSR within the bound of a maximum acceptable latency. Random forest-based
prediction models are trained to predict the perceptual quality after VSR and
the encoding time for each resolution using the spatiotemporal features
extracted for each video segment. Experimental results show that ViSOR
targeting fast super-resolution convolutional neural network (FSRCNN) achieves
an overall average bitrate reduction of 24.65 % and 32.70 % to maintain the
same PSNR and VMAF, compared to the HTTP Live Streaming (HLS) bitrate ladder
encoding of 4 s segments using the x265 encoder, when the maximum acceptable
latency for each representation is set as two seconds. Considering a just
noticeable difference (JND) of six VMAF points, the average cumulative storage
consumption and encoding energy for each segment is reduced by 79.32 % and
68.21 %, respectively, contributing towards greener streaming.
