idash

iDASHImproved DASH using SVCYigit UNALLAR

• HTTP-based delivery for VoD,• Congestion in Cache Feeder/ Access Link,• Connection Characteristics vary over time, • Dynamically adapt the requested video quality, • Scalable Video Coding for DASH,

Introduction

• HTTP/TCP over RTP/UDP,✓ Immune to firewall and NAT traversal issues, ✓ Substantially relieve the load on the video server by

using HTTP cache,

• DASH,✓ Content in various versions, ✓ To alleviate the congestion, switching occurs, ✓ Segmenting each version in chunks, ✓ Multiple Representation of each video(H.264/AVC),✓ All reps in one file (SVC)

Introduction

• Minimizing the transmitted data,

• Relieving the server,

How to select every moment what is in the cache?

Related Work

• Most algorithms rely on,✓ LRU,

✓ LFU,

✓ Combination thereof,

• In this work, ✓ Chunks to be consumed predicted,

o User playing nth chunk will play n+kth chunk of the same video k time instants later!!

Related Work

• Serving a great number of users w/ a single bit stream,

• Coping w/ congestion by applying on-the-fly adaptation,

• OP refers to a valid sub-stream at a certain quality level and a corresponding bit rate,

How to encode multiple quality layers?

• CGS or MGS,o CGS, the number of OPs is the same as the number of

layers,

o MGS, switching on a NAL unit basis, much higher number of possible Ops,

Efficient Encoding w/ SVC

• So many layers => so many Ops => coding overhead!!,

• Encode a reduced number of quality layers w/ MGS


Different OPs are obtained by dropping enhancement layer packets!

• Dropping Q2(blue) in T2& T3 levels,

• Dropping the Q2 of all temporal levels and pictures from Q1(red) of T2& T3 levels,


OPS must be selected in such a way that users experience smooth quality degradation!

• Sims are based on real data(Observation of deployed VoD service),

• 1 month,• More than 500 movies offered,• Average 3400 reqs per day,

Model

• Caching Algorithms,

➢ LRU,➢ Most recently requested chunks are kept,

➢ CC,➢ Number of guaranteed hits of chunks, LRU and LFU

combined,

➢ SVC might score more cache-hit-ratio as opposed to AVC,

Model

• Congestion in cache feeder?➢ @ peak hours, cache misses and updates are

higher!➢ Current infra. Not sufficient, congestion occurs,

Data received with delay, thus ramping down,Waiting timer set after first ramp-down, if equal,

ramps up,

• Congestion in access links?

Model

• Congestion in cache feeder?

• Congestion in access links?

Markov Chain, to describe transition probabilities, utilized!

Model

• Cache-hit-ratio,➢ Percentage that objects can be served from the

cache,• Not downgrading clients,

➢ Percentage…,• D/R

➢ Desired/ Received rate,

Performance Targets

ResultsCase 1: DASH Clients requesting too many data in peak hours,

Case 2: Cross-traffic due to DASH clients sharing resources,

•Videos comprised of 90 mins and chunks of 10 secs length,


CHR,7 % up for low cache cap,

ND,lower coding efficiency,

D/R,Reutilization of already cached video content,




Different versions of the req videosspoil the cache!

Any Questions?

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Engineering