Live video streaming is widely embraced in video services, and its applications have attracted much attention in recent years. The increased number of users demanding high quality (e.g., 4K resolution) live videos increase the bandwidth utilization in the backhaul network. To decrease bandwidth utilization in HTTP Adaptive Streaming (HAS), in on-the-fly transcoding approaches, only the highest bitrate representation is delivered to the edge, and other representations are generated by transcoding at the edge. However, this approach is inefficient due to the high transcoding cost. In this paper, we propose a light-weight transcoding at the edge method for live applications, LwTE-Live, to decrease the band-width utilization and the overall live streaming cost. During the encoding processes at the origin server, the optimal encoding decisions are saved as metadata, and the metadata replaces the corresponding representation in the bitrate ladder. The significantly reduced size of the metadata compared to its corresponding representation decreases the bandwidth utilization. The extracted metadata is then utilized at the edge to decrease the transcoding time. We formulate the problem as a Mixed-Binary Linear Programming (MBLP) model to optimize the live streaming cost, including the bandwidth and computation costs. We compare the proposed model with state-of-the-art approaches and the experimental results show that our proposed method saves the cost and backhaul bandwidth utilization up to 34% and 45%, respectively.

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LwTE-Live: Light-weight Transcoding at the Edge
for Live Streaming
ViSNext’21: 1st ACM CoNEXT Workshop on Design, Deployment,
and Evaluation of Network-assisted Video Streaming)
December 7th 2021
Alireza Erfanian, Hadi Amirpour, Farzad Tashtarian, Christian Timmerer, and Hermann Hellwagner
Christian Doppler laboratory ATHENA | Klagenfurt University | Austria
alireza.erfanian@aau.at | https://athena.itec.aau.at/

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● Introduction
● Proposed approach
● Results
● Conclusion and future work
● Q&A
All rights reserved. ©2020
TABLE OF CONTENTS

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Live video streaming
Live streaming: a specific type of streaming that
media broadcasts in real-time (it can be pre-recorded
or simultaneously recorded) such as:
● News, concerts, sports or awards shows
● Conferences
● Educational events
● Gaming
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Source: http://statista.com/

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Live streaming: main challenges
1. End-to-end (E2E) latency is a critical QoE
parameter, conflicting with other QoE
parameters such as video quality, low
rebuffering rate, and few quality
switches.
2. Scalability: a huge number of requests
especially for popular events leads to
congestion in the network and undesirable
performance at the origin server.
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Throughput

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HAS Player
Distribution
Internet
Ingest
CDN Server
CDN Server
CDN Server
Origin Server
ABR
Encoder
Live
Source
HAS Player
HAS Player
Delivery
CDN-enable live streaming workflow
Introduces high
bandwidth utilization in
the network backhaul,
especially between CDN
servers and HAS players
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HAS Player
Distribution
Ingest
CDN Server
CDN Server
Origin Server
ABR
Encoder
Live
Source
HAS Player
HAS Player
Edge Server
Edge Server
Edge Server
Delivery
Internet
Edge-enable live streaming workflow
Decrease the backhaul
network utilization
Introduce higher E2E
latency due to requests
aggregation at the edge
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HAS Player
Distribution
Ingest
CDN Server
CDN Server
Origin Server
ABR
Encoder
Live
Source
HAS Player
HAS Player Edge Server
Edge Server
Edge Server
Delivery
Internet
Edge-transcoding enable live streaming workflow
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Decrease the backhaul network utilization
Higher E2E latency while transcoding is
the compute-intensive and
time-consuming process

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LwTE: Light-weight Transcoding at the Edge [1]
● In our previous work [1], we propose a new lightweight transcoding method at the edge named
LwTE that reduces the transcoding time up to 80% compared with the conventional
transcoding method.
● The main idea of LwTE is to extract some features as metadata during the encoding process in
the origin server and reuse them in the transcoding process to reduce the transcoding time
and computational costs.
[1] A. Erfanian, H. Amirpour, F. Tashtarian, C. Timmerer and H. Hellwagner, "LwTE: Light-Weight Transcoding at the Edge," in IEEE Access, vol. 9,
pp. 112276-112289, 2021
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Motivating example
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How to decrease the live streaming cost including the computation
and bandwidth costs while increase scalability by saving network
backhaul bandwidth considering the following constraints:
1. Bandwidth limitation between edge and CDN/origin server
2. Available computation resource at the edge
3. E2E latency

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Proposed Mixed-Binary Linear Programming (MBLP) model
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Bandwidth
cost
Computation
cost
Required computation
resources for
transcoding
Total amount of data
fetched from CDN/origin
server
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Serving policy constraints that
select an optimal policy,
including transcoding at the
edge server or fetching from
the origin/CDN server
● Select a policy for each request
● Selects an instance for request 𝑟 if the
transcoding policy has been chosen for 𝑟
● Guarantees that at least one
representation with higher bitrate than 𝑟
must be downloaded from the
origin/CDN server

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Proposed Mixed-Binary Linear Programming (MBLP) model
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Serving time constraints that
guarantee to meet the given
deadline for serving all requests
● Determines the total transcoding
time by considering the available
computation resources
● Determines the total fetching time
by taking into account the available
bandwidth between edge and the
origin/CDN server
● Guarantees that total transcoding
time + total fetch time be less than
the given deadline
Computation resource constraints
that determine the required
computation resources for
transcoding operations at the edge
server
● Determines the required computation
resources for transcoding operations at
the edge server
● Guarantees that the largest selected
instance does not exceed the available
computation resources at the edge
server
● Guarantees to provide the required
computation resources during the
transcoding time

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Performance Evaluation
● Ten minutes live streaming session
● Transcoding is performed on Amazon EC2 instances with various resource profiles
● Default values for segment length = 2 sec, available computation resources = 16 CPU cores,
and bandwidth between edge and origin/CDN server = 100𝑀𝑏𝑝𝑠
● Compare with the following state-of-the-art approaches
○ OSCAR [1]: determines the optimal policy, including fetching and transcoding, from a
higher representation through the conventional transcoding approach, for serving the
incoming request at the edge server.
○ FetchAll: does not perform any transcoding, and the clients’ requested segments are
fetched from the origin/CDN server after aggregation at the edge sever.
[1] A.Erfanian, F.Tashtarian, A.Zabrovskiy, C.Timmerer, and H.Hellwagner. 2021. “OSCAR: On Optimizing Resource Utilization in Live Video
Streaming.” IEEE Transactions on Network and Service Management 18, 1 (2021), 552–569
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Evaluation results: LwTE’s performance in terms of (a) Transcoding time saving of the
proposed method (with metadata) compared to the conventional method (without metadata), (b)
relative bitrates of metadata to its corresponding representations
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Evaluation results: Performance of the proposed model and compare it with state-of-the-art
approaches’ in terms of (c) bandwidth utilization, (d) normalized cost
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Evaluation results: Performance comparison of LwTE-Live with state-of-the-art approaches
in terms of (a) normalized cost, (b) bandwidth utilization, (c) CPU utilization, and (d) number of
transcoded representations for various bandwidth profiles (60, 100, 150, and 200 Mbps)
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Evaluation results: Performance comparison of LwTE-Live with state-of-the-art approaches
in terms of (a) normalized cost, (b) bandwidth utilization, (c) CPU utilization, and (d) number of
transcoded representations for various segment lengths (1sec, 2sec, 4sec)
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Conclusion and Future Work
● Investigate the cost efficiency of LwTE for live streaming applications.
● Formulate the problem of minimizing live streaming cost by selecting the
optimal policy, including fetching and transcoding, for serving incoming
requests to the edge server as a MBLP model.
● Compare the proposed method with state-of-the-art approaches in terms of live
streaming cost and backhaul network utilization.
● Show that our proposed method saves the cost and backhaul bandwidth
utilization up to 34% and 45%, respectively.
● Extending LwTE-live to support multiple video channels and relaxing
assumptions is subject to future work direction.
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