Most current-generation P2P content distribution protocols use fine-granularity blocks to distribute content in a decentralized fashion. Such systems often suffer from a significant variation in block distributions, such that certain blocks become rare or even unavailable, adversely affecting content availability and download efficiency. This phenomenon is further aggravated by peer dynamics which is inherent in P2P networks. In this thesis, we quantitatively analyze how network coding may improve block availability and introduce resilience to peer dynamics. Since in reality, network coding can only be performed within segments, each containing a subset of blocks, we explore the fundamental tradeoff between the resilience gain of network coding and its inherent coding complexity, as the number of blocks in a segment varies. As another application of the resilience of network coding, we also devise an indirect data collection scheme based on network coding for the purpose of large-scale network measurements.

Most current-generation P2P content distribution protocols use fine-granularity blocks to distribute content in a decentralized fashion. Such systems often suffer from a significant variation in block distributions, such that certain blocks become rare or even unavailable, adversely affecting content availability and download efficiency. This phenomenon is further aggravated by peer dynamics which is inherent in P2P networks. In this thesis, we quantitatively analyze how network coding may improve block availability and introduce resilience to peer dynamics. Since in reality, network coding can only be performed within segments, each containing a subset of blocks, we explore the fundamental tradeoff between the resilience gain of network coding and its inherent coding complexity, as the number of blocks in a segment varies. As another application of the resilience of network coding, we also devise an indirect data collection scheme based on network coding for the purpose of large-scale network measurements.

This Master Thesis report was written by Umeå University Engineering Physics student Richard Tano during his thesis work at Ericsson Luleå. Monitoring network quality is of utmost importance to network providers. This can be done with models evaluating QoS (Quality of Service) and conforming to ITU-T Recommendations. When determining video stream quality there is of more importance to evaluatethe QoE (Quality of Experience) to understand how the user perceives the quality. This isranked in MOS (Mean opinion scores) values. An important aspect of determining the QoEis the video content type, which is correlated to the coding complexity and MOS values ofthe video. In this work the possibilities to improve quality estimation models complying to ITU-T study group 12 (q.14) was investigated. Methods were evaluated and an algorithm was developed that applies time series analysis of packet statistics for determination of videostreams MOS scores. Methods used in the algorithm includes a novel assembling of frequentpattern analysis and regression analysis. A model which incorporates the algorithm for usage from low to high bitrates was dened. The new model resulted in around 20% improvedprecision in MOS score estimation compared to the existing reference model. Furthermore an algorithm using only regression statistics and modeling of related statistical parameters was developed. Improvements in coding estimation was comparable with earlier algorithm but efficiency increased considerably. / Detta examensarbete skrevs av Richard Tano student på Umeå universitet åt Ericsson Luleå. Övervakning av nätets prestanda är av yttersta vikt för nätverksleverantörer. Detta görs med modeller för att utvärdera QoS (Quality of Service) som överensstämmer med ITU-T rekommendationer. Vid bestämning av kvaliten på videoströmmar är det mer meningsfullt att utvärdera QoE (Quality of Experience) för att få insikt i hur användaren uppfattar kvaliten. Detta graderas i värden av MOS (Mean opinion score). En viktig aspekt för att bestämma QoE är typen av videoinnehåll, vilket är korrelerat till videons kodningskomplexitet och MOS värden. I detta arbete undersöktes möjligheterna att förbättra kvalitetsuppskattningsmodellerna under uppfyllande av ITU-T studygroup 12 (q.14). Metoder undersöktes och en algoritm utvecklades som använder tidsserieanalys av paketstatistik för uppskattning av videoströmmars MOS-värden. Metoder som ingår i algoritmen är en nyutvecklad frekventa mönster metod tillsammans med regressions analys. En modell som använder algoritmen från låg till hög bithastighet definierades. Den nya modellen gav omkring 20% förbättrad precision i uppskattning av MOS-värden jämfört med existerande referensmodell. Även en algoritm som enbart använder regressionsstatistik och modellerande av statistiska parametrar utvecklades. Denna algoritm levererade jämförbara resultat med föregående algoritm men gav även kraftigt förbättrad effektivitet.