{"id":"W2520420524","doi":"10.1109/icccn.2016.7568513","title":"EDASH: Energy-Aware QoE Optimization for Adaptive Video Delivery over LTE Networks","year":2016,"lang":"en","type":"article","venue":"","topic":"Green IT and Sustainability","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Quality of experience; Computer network; Dynamic Adaptive Streaming over HTTP; Energy consumption; Throughput; Quality of service; Efficient energy use; Dash; Bandwidth (computing); Real-time computing; Wireless; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007857969,0.0001300949,0.000123807,0.0000409666,0.00004856739,0.00001754391,0.00008309238,0.0001061365,0.0003372783],"category_scores_gemma":[0.00001411116,0.00009340487,0.00007534667,0.0000844409,0.0000218592,0.0002282573,0.00002561812,0.00003506694,0.000003349313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001362345,"about_ca_system_score_gemma":0.00001618809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004796282,"about_ca_topic_score_gemma":0.00008272957,"domain_scores_codex":[0.9993316,0.00001318489,0.0001514261,0.0001657277,0.00007447651,0.0002635689],"domain_scores_gemma":[0.9994863,0.0001217633,0.00001632759,0.0001822319,0.0001269106,0.00006649851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006775067,0.00001740402,0.001176645,0.00002676749,0.00005429129,0.000001837625,0.00004214565,0.9319091,0.00004037762,0.003285493,0.0167558,0.04662242],"study_design_scores_gemma":[0.0004345132,0.00003995647,0.0004123194,0.00001506875,0.00001251863,4.05677e-7,0.00004785557,0.9936708,0.0002222967,0.0005583184,0.004408211,0.0001777461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007154081,0.00009747583,0.9900158,0.00005911222,0.0002357081,0.0001543121,0.00000940638,0.0002744244,0.001999667],"genre_scores_gemma":[0.9944475,0.0000499185,0.003432174,0.0000566515,0.0001737759,0.00005647636,0.000009583513,0.00003092394,0.001743013],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9872934,"threshold_uncertainty_score":0.380894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006327403328234015,"score_gpt":0.1827401647486409,"score_spread":0.1764127614204068,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}