Two-level sliding-window VBR control algorithm for video on demand streaming
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
In: Signal Processing: Image Communication (36:) - Amsterdam [u.a.] : Elsevier - S. 1-13
- Ereignis
-
Veröffentlichung
- (wo)
-
Berlin
- (wer)
-
Technische Universität Berlin
- (wann)
-
2015
- Urheber
-
Frutos-López, Manuel de
González-de-Suso, José Luis
Sanz-Rodríguez, Sergio
Peláez-Moreno, Carmen
Díaz-de-María, Fernando
- DOI
-
10.14279/depositonce-6786
- Handle
-
11303/7572
- URN
-
urn:nbn:de:101:1-201804185256
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:56 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Frutos-López, Manuel de
- González-de-Suso, José Luis
- Sanz-Rodríguez, Sergio
- Peláez-Moreno, Carmen
- Díaz-de-María, Fernando
- Technische Universität Berlin
Entstanden
- 2015
Ähnliche Objekte (12)
Language recognition in the sliding window model
Derandomization for Sliding Window Algorithms with Strict Correctness∗
Computing Minimal Unique Substrings for a Sliding Window
Sliding Window Empirical Mode Decomposition -its performance and quality
Sliding Window Top-K Monitoring over Distributed Data Streams
Potentiale von On-Demand-Musik-Streaming
Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives
Diagnosis of model errors with a sliding time‐window Bayesian analysis
How secure is exponent-blinded RSA–CRT with sliding window exponentiation?
A training sequence parallel detection technology based on timeslot sliding window
Graph-based Sliding Window Localization and Map Refinement for Automated Vehicles
Versatile time-window sliding machine learning techniques for stock market forecasting
Language recognition in the sliding window model
Derandomization for Sliding Window Algorithms with Strict Correctness∗
Computing Minimal Unique Substrings for a Sliding Window
Sliding Window Empirical Mode Decomposition -its performance and quality
Sliding Window Top-K Monitoring over Distributed Data Streams
Potentiale von On-Demand-Musik-Streaming
Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives
Diagnosis of model errors with a sliding time‐window Bayesian analysis
How secure is exponent-blinded RSA–CRT with sliding window exponentiation?
A training sequence parallel detection technology based on timeslot sliding window
Graph-based Sliding Window Localization and Map Refinement for Automated Vehicles
Versatile time-window sliding machine learning techniques for stock market forecasting
Language recognition in the sliding window model
Derandomization for Sliding Window Algorithms with Strict Correctness∗
Computing Minimal Unique Substrings for a Sliding Window
Sliding Window Empirical Mode Decomposition -its performance and quality
Sliding Window Top-K Monitoring over Distributed Data Streams
Potentiale von On-Demand-Musik-Streaming
Better Sliding Window Algorithms to Maximize Subadditive and Diversity Objectives
Diagnosis of model errors with a sliding time‐window Bayesian analysis
How secure is exponent-blinded RSA–CRT with sliding window exponentiation?
A training sequence parallel detection technology based on timeslot sliding window
Graph-based Sliding Window Localization and Map Refinement for Automated Vehicles