{"id":"W4416965676","doi":"10.1109/ojcoms.2025.3639583","title":"Cache-Enabled XR Systems: Delay-Aware Resource Allocation for Immersive Experience","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"King Abdullah University of Science and Technology","keywords":"Cache; Mobile edge computing; Knapsack problem; Base station; Rendering (computer graphics); Quality of experience; Enhanced Data Rates for GSM Evolution; Resource allocation; Optimization problem; Redundancy (engineering)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003960331,0.0006206169,0.0004882893,0.0002367354,0.000246905,0.0008448734,0.0009216918,0.0004625952,0.001852458],"category_scores_gemma":[0.001103741,0.0002483558,0.0002836786,0.0003847552,0.0002808106,0.001130548,0.00095809,0.0006714925,0.0003855608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004416639,"about_ca_system_score_gemma":0.0005398263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002015284,"about_ca_topic_score_gemma":0.0031022,"domain_scores_codex":[0.9997174,0.00008721276,0.00001110283,0.00005185396,0.00009046744,0.00004199626],"domain_scores_gemma":[0.999718,0.0001136865,0.00003992898,0.00003696784,0.00006206123,0.00002934171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003983892,0.0001904475,0.001325813,0.0002059073,0.00008422147,0.0003044787,0.0002886791,0.6276748,0.06566276,0.01469353,0.004946551,0.2842245],"study_design_scores_gemma":[0.00001567005,0.0001211865,0.0002206251,0.000009546646,0.00001765629,0.0001050151,0.0000468736,0.9907635,0.00484815,0.001794308,0.002043369,0.00001423271],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02387381,0.0006440502,0.9724754,0.0001117612,0.00003068205,0.00002669667,0.00003830128,0.0003893987,0.002409951],"genre_scores_gemma":[0.7318557,0.0008720669,0.2634477,0.0001420687,0.00006957409,0.0000734652,0.0001018904,0.0001255954,0.003312075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002015284,"threshold_uncertainty_score":0.006197095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07770609724867006,"score_gpt":0.3916697629352032,"score_spread":0.3139636656865331,"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."}}