Optimizing carbon emissions from streaming platforms with artificial intelligence based model
Media is streamed in accordance with carbon footprint considerations. A streaming history of a streaming plan is analyzed to determine a historical carbon footprint. At least one streaming plan is presented that includes target carbon footprint relative to the historical carbon footprint. A selection is received for one of the at least one streaming plan, wherein the streaming performance on the streaming plan is tracked for post streaming plan carbon emissions. The user streaming performance is modified for the post streaming plan carbon emissions to substantially match the target carbon footprint of the at least one streaming plan.
1 . A computer-implemented method for optimizing streaming media comprising:
analyzing a streaming history of a streaming subscription to determine a historical carbon footprint;
training a neural network to forecast an upcoming carbon footprint based on the historical carbon footprint by matching search items extracted from user requirements and source code stored in repositories to obtain a trained neural network;
producing, using the trained neural network, at least one streaming plan that includes a target carbon footprint relative to the upcoming carbon footprint;
receiving a selected streaming plan for one of the at least one streaming plan having a target carbon emissions, wherein a streaming performance on the selected streaming plan is tracked for post streaming plan carbon emissions in carbon unit of measure;
maximizing the streaming performance and minimizing the carbon emissions using multi-objective optimization during streaming by dynamically modifying the streaming performance including:
dividing content being broadcast into frames by performing video segmentation of the content being broadcast responsive to a temporal change meeting a threshold value;
analyzing an aesthetic quality of the frames based on frame content parameters to determine a carbon output for the frames;
training a multi-modal model with historical trends of carbon emissions per calendar time periods for the streaming media being broadcast based on the carbon output for the frames to obtain a trained multi-modal model;
predicting, with the trained multi-modal model, an upcoming carbon footprint as a streaming performance of content per frame for carbon units being produced from the streaming media; and
adjusting, with the trained multi-modal model, the frames into carbon-aware frames by optimizing the carbon units produced by the frames based on user streaming characteristics and the aesthetic quality of the frames to match the target carbon footprint of the selected streaming plan and the upcoming carbon footprint in real time including reducing resolution for images including only black and white text, and increasing resolution for images of motion and color images.
2 . The computer-implemented method of claim 1 , wherein the at least one streaming plan includes a type of devices on which streaming media can be viewed.
3 . The computer-implemented method of claim 1 , wherein the at least one streaming plan includes a number of devices on which streaming media can be viewed.
4 . The computer-implemented method of claim 1 , wherein the at least one streaming plan includes a resolution for the streaming media to be viewed.
5 . The computer-implemented method of claim 1 , wherein maximizing the streaming performance further comprises selecting a quality for image resolution selected from a group consisting of standard definition, high definition, full high definition, and 4K.
6 . A system for optimizing streaming media comprising:
a hardware processor; and
a memory that stores a computer program product, which, when executed by the hardware processor, causes the hardware processor to:
analyze a streaming history of a streaming subscription to determine a historical carbon footprint;
train a neural network to forecast an upcoming carbon footprint based on the historical carbon footprint by matching search items extracted from user requirements and source code stored in repositories to obtain a trained neural network;
present, using the trained neural network, at least one streaming plan includes a target carbon footprint relative to the upcoming carbon footprint;
receive a selected streaming plan for one of the at least one streaming plan having a target carbon emissions, wherein a streaming performance on the selected streaming plan is tracked for post streaming plan carbon emissions in carbon unit of measure;
maximize the streaming performance and minimize the carbon emissions using multi-objective optimization during streaming by dynamically modifying the streaming performance including:
divide content being broadcast into frames by performing video segmentation of the content being broadcast responsive to a temporal change meeting a threshold value;
analyze an aesthetic quality of the frames based on frame content parameters to determine a carbon output for the frames;
train a multi-modal model with historical trends of carbon emissions per calendar time periods for the streaming media being broadcast based on the carbon output for the frames to obtain a trained multi-modal model;
predict, with the trained multi-modal model, an upcoming carbon footprint as a streaming performance of content per frame for carbon units being produced from the streaming media; and
adjust, with the trained multi-modal model, the frames into carbon-aware frames by optimizing the carbon units produced by the frames based on user streaming characteristics and the aesthetic quality of the frames to match the target carbon footprint of the selected streaming plan and the upcoming carbon footprint in real time including reducing resolution for images including only black and white text, and increasing resolution for images of motion and color images.
7 . The system of claim 6 , wherein the at least one streaming plan includes a type of devices on which streaming media can be viewed.
8 . The system of claim 6 , wherein the at least one streaming plan includes a number of devices on which streaming media can be viewed.
9 . The system of claim 6 , wherein the at least one streaming plan includes a resolution for the streaming media to be viewed.
10 . The system of claim 6 , wherein maximizing the streaming performance further comprises selecting a quality for image resolution selected from a group consisting of standard definition, high definition, full high definition, and 4K.
11 . A computer program product for optimizing streaming media comprising a computer readable storage medium having computer readable program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:
analyze, using the processor, a streaming history of a streaming subscription to determine a historical carbon footprint of a user's streaming history;
train a neural network to forecast an upcoming carbon footprint based on the historical carbon footprint by matching search items extracted from user requirements and source code stored in repositories to obtain a trained neural network;
produce, using the trained neural network, at least one streaming plan includes a target carbon footprint relative to the upcoming carbon footprint;
receive, using the processor, a selected streaming plan for one of the at least one streaming plan having a target carbon emissions, wherein a streaming performance on the selected streaming plan is tracked for post streaming plan in carbon unit of measure;
maximize the streaming performance and minimize the carbon emissions using multi-objective optimization during streaming by dynamically modifying the streaming performance including:
divide content being broadcast into frames by performing video segmentation of the content being broadcast responsive to a temporal change meeting a threshold value;
analyze an aesthetic quality of the frames based on frame content parameters to determine a carbon output for the frames;
train a multi-modal model with historical trends of carbon emissions per calendar time periods for the streaming media being broadcast based on the carbon output for the frames to obtain a trained multi-modal model;
predict, with the trained multi-modal model, an upcoming carbon footprint as a streaming performance of content per frame for carbon units being produced from the streaming media; and
adjust, with the trained multi-modal model, the frames into carbon-aware frames by optimizing the carbon units produced by the frames based on the user streaming characteristics and the aesthetic quality of the frames to match the target carbon footprint of the selected streaming plan and the upcoming carbon footprint in real time including reducing resolution for images including only black and white text, and increasing resolution for images of motion and color images.
12 . The computer program product of claim 11 , wherein the at least one streaming plan includes a type of devices on which streaming media can be viewed.
13 . The computer program product of claim 11 , wherein the at least one streaming plan includes a number of devices on which streaming media can be viewed.
14 . The computer program product of claim 11 , wherein the at least one streaming plan includes a resolution for the streaming media to be viewed.
15 . The computer program product of claim 11 , wherein maximizing the streaming performance further comprises selecting a quality for image resolution selected from a group consisting of standard definition, high definition, full high definition, and 4K.
16 . The computer-implemented method of claim 1 , wherein the frame content parameters include content information, object dynamics, and personalization.
17 . The system of claim 6 , wherein the user streaming characteristics includes streaming quality, device type for viewing streaming media being broadcast as a function of low carbon producing standard definition content or high carbon producing high definition content for the post streaming plan carbon emissions, network type, streaming duration.
18 . The computer program product of claim 11 , wherein analyzing the aesthetic quality further comprises detecting pixel-wise objects that depict movement in the frames.